Oklahoma Behavioral Healthcare Workforce Study Final Report 6 21 11 |
Previous | 1 of 4 | Next |
|
small (250x250 max)
medium (500x500 max)
Large
Extra Large
large ( > 500x500)
Full Resolution
|
This page
All
|
1 OKLAHOMA BEHAVIORAL HEALTHCARE WORKFORCE STUDY: DRAFT STATEWIDE REPORT · Separation Rates & Staff Intention to Leave · Vacancy Rates · Organizational Benefits & Staff Pay Rates · Staff Work Experience & Job Satisfaction Oklahoma Behavioral Healthcare Workforce Study: Final Statewide Report June 21, 2011 This research was supported by the Mental Health Transformation State Incentive Grant, 5U79SM057411 from the U.S Department of Health and Human Services, Substance Abuse and Mental Health Services Administration. The views expressed do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices or organizations imply endorsement by the U.S. Government. Principal Contributors include: John Hornik, Ph.D., Jenneth Carpenter, Ph.D., and Jeanine Hanna, Ph.D., Advocates for Human Potential, Albany, NY and David Wright, Ph.D. and Lorrie Byrum, M.A., Oklahoma Dept. of Mental Health and Substance Abuse Services, Oklahoma City, OK. Please see page 7 of the report for acknowledgement of the many other contributors to this study. 2 Oklahoma Behavioral Healthcare Workforce Study Statewide Report TABLE OF CONTENTS Executive Summary 3 Acknowledgements 7 Chapter 1: Introduction 9 Chapter 2: Staff Separations 16 Chapter 3: Vacancies and Staff Recruitment Barriers 27 Chapter 4: Current and Future Staffing Needs 33 Chapter 5: Benefits and Compensation 47 Chapter 6: Staff Work Experience and Job Satisfaction 56 Chapter 7: Workforce Capacity 62 Chapter 8: Representation of Consumers and Their Family Members in the Workforce 72 Chapter 9: Discussion and Recommendations 80 References 84 Appendix A: Staff Separations 85 Appendix B: Vacancies and Recruitment Barriers 98 Appendix C: Benefits and Compensation 100 Appendix D: Work Experience and Job Satisfaction 102 Appendix E: Workforce Capacity 107 3 EXECUTIVE SUMMARY The Oklahoma Behavioral Healthcare Workforce Survey and associated studies were conducted by Advocates for Human Potential, Inc. (AHP) through a contract with the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS) to assist with evaluation activities related to Oklahoma’s behavioral health transformation initiative. The statewide survey included three components: · an organizational survey focused on organizational accreditation, benefits and basic information on organizational structure; · a program manager survey related to program staffing, vacancy, recruitment barriers, causes of staff turnover, program and staff capacity and training needs; and · a staff survey focused on work experience, job satisfaction, education, training, and demographic characteristics (including status as current or prior consumers or family members of consumers). The data collection process was structured so that the three components could be linked. Participating organizations were recruited in industry groups, generally according to state agency funding and oversight. While the workforce survey was the largest component of this project and is the primary focus of this report, additional data sources were used. These include: Economic Modeling Systems, Inc. (EMSI) data provided by the Oklahoma Department of Commerce; Oklahoma data drawn from a University of North Carolina (UNC) staffing needs study; and information on historical and anticipated behavioral healthcare-related degree completion rates from the Oklahoma State Regents of Higher Education. The results of the survey and the analysis of additional data sources were reviewed with stakeholders, including external key informants, informants involved with the project, and a Workforce Study Team convened as an advisory group to the study. Based on these reviews, the following key findings were identified: · Inadequate salaries are widespread and are believed to have significant implications for both recruitment and retention. Over half of all staff members responding to the survey reported earning less than $15.00 per hour, with close to one-fifth earning less than $10.00 per hour. Assuming a 40-hour workweek, staff in this latter pay group only fall above the 2009/2010 poverty line if they have no dependents. Those with one or more dependents are living in poverty, despite being employed full-time in a challenging and critical industry. Not surprisingly, less than half of responding staff indicated that they were satisfied or very satisfied with their pay. Over half of all responding program managers also identified insufficient salary as one of the top barriers to recruiting qualified staff for their programs, and nearly two-thirds identified dissatisfaction with salary as one of the top causes of staff turnover in their programs. Indeed, pay was both the most frequently cited barrier to recruitment and most frequently cited cause of turnover. · Staff separation rates are high and relate to the composition of the workforce. The median annual program separation rate was 25%, meaning that, in roughly half of the 4 participating programs, there was more than one staff departure within the past year for every four FTEs. Separation rates do not vary randomly, but rather are associated with program staffing patterns. While staff were given six position categories from which to describe their jobs, nearly all chose either counselor/therapist/social worker or aide/tech/other paraprofessional. On average, counselors made up 50% of program staff, while techs made up 39%. Program managers reported separation rates of 42% and 25% for techs and counselors, respectively. This finding is consistent with the literature indicating that higher staff experience, job level, and pay are associated with lower turnover. · There are both current and projected shortages of professional and nonprofessional staff with an insufficient pipeline of new entrants from higher education to meet the shortages. There is a substantial gap in the need for psychiatrists and other prescribers. We estimate a need for 697 prescribers and only 287 professionals (psychiatrists and advanced practice psychiatric nurses) available to meet the need, a difference of 410. While the unmet needs for other categories of behavioral healthcare providers are not as large proportionately, there are gaps in these position types as well. The rates at which institutions of higher education in Oklahoma are producing new graduates with appropriate training are not sufficient to meet these needs; in addition, the fact that salaries for both professional and nonprofessional positions in Oklahoma are consistently lower than the surrounding states and the nation as a whole is a significant barrier to attracting new individuals into service or training. · A substantial proportion of responding staff and program managers self-identify as behavioral healthcare consumers or as family members of consumers. Both program managers and staff were asked a series of questions about their status as consumers, defined as someone who is currently or has received mental health, substance abuse, and/or other addictive disorder services, or as a family member of a consumer. Nearly one-third of respondents identified as family members and over one-fifth identified as adult consumers. Consumer and family member representation was generally higher among program managers than direct care staff, and a greater proportion of respondents identified as adult consumers than as (former) youth consumers. Additionally, among staff and program managers who identified as either consumers or family members, rates of disclosure in the workplace were high. For both statuses, roughly 80% of responding program managers reported disclosing on the job, while roughly 66% of staff reported having disclosed. · Staff are relatively well-prepared to offer Cognitive Behavioral Therapy (CBT) and are less prepared to offer other Evidence-Based Practices (EBPs). Nearly three-quarters of respondents who supervise programs serving adults indicated that new, professional-level hires in their programs were well-prepared to provide CBT. The same percentage of respondents who supervise programs serving children reported that their new, professional-level hires were prepared to offer services using CBT for trauma, while over two-thirds reported that their new, professional-level hires could provide CBT for anxiety and depression. In both child and adult-serving programs, fewer program managers reported staff competence in providing other types of EBPs: For example, just over one-third of program managers supervising adult programs reported staff competence in medication management, 5 and only a little more than half of those supervising programs for children reported staff competence in interpersonal therapy (IPT). · Knowledge of psychiatric medication and its side-effects is the most common unmet training need. Program managers were asked to identify areas of unmet training need or areas in which their staff needed training and for which training was not readily available. The most frequently cited unmet training need was for knowledge of psychiatric medication and its side-effects, with one-quarter of responding program managers citing this as an unmet need. Nearly as many program managers (23%) indicated that their staff had unmet needs for communication skills training, while the third most frequently cited unmet need (17%) was for training in educating consumers’ family members about mental health and substance abuse issues. All other competencies were cited as unmet training needs by fewer than 15% of responding program managers. · Staff report high job satisfaction and a positive overall work experience. Nearly all (95%) staff respondents agreed or strongly agreed with the statement I like the kind of work that I do and 84% of staff respondents indicated that they were satisfied or very satisfied with their job overall. Many of the more specific indicators of job satisfaction and work experience were also endorsed by the majority of staff. In particular, over three-quarters of responding staff indicated that their work gives them a feeling of personal accomplishment and that they would recommend their organization as a good place to work. Similarly, over two-thirds of those responding indicated that they were satisfied with their organization, their work schedule, and the location and physical conditions of their workplace. Lower rates of satisfaction were found with pay (described earlier), opportunity for advancement, and workplace stress level. Nearly three-quarters of respondents reported being satisfied with vacation and sick leave, with state employees generally reporting higher satisfaction with benefits than those employed by private organizations. While the generalizability of these findings is limited somewhat by the staff survey response rate and the potential for selection bias (i.e., the possibility that staff who responded were more satisfied with their work), the overwhelmingly positive response to these items is worth noting. The report concludes with a review of the Workforce Study Team’s recommendations for next steps. Throughout these recommendations, the Workforce Study Team identified the need to distinguish between strategies to maintain the behavioral healthcare workforce in its current state, and those to facilitate the development of a workforce that would be fully responsive to the behavioral healthcare needs of Oklahoma’s citizens. Regarding compensation, the Team advised that current pay rates are inadequate and suggested preparing a legislative request to bring behavioral health provider pay to the regional average by 2014. The Team also suggested increasing opportunities for advancement within behavioral health organizations to alleviate recruitment and retention problems within the field, as well as providing incentives for students to receive a portion of their clinical training in state-funded service systems. Several training-related recommendations were made to increase the number of prescribers in the state and support the development of basic behavioral healthcare skills among primary medical care providers. Implementing best practices was cited as a way to respond to the study’s findings regarding staff paperwork burden as related to job satisfaction and causes of turnover. Specific recommendations regarding best practices included: expanding access to the most up-to-date 6 information on evidence-based practices; technical assistance for professionals providing mental health services and substance abuse services in state agencies; and limiting the quantity of mandatory paperwork and reporting. Finally, the Team recommended that future planning efforts include creating a Mental Health and Substance Abuse Workforce Advisory Council to help Oklahoma develop models for providing behavioral healthcare services for its citizens in the future and meeting the prospective workforce needs for Oklahoma’s future. 7 ACKNOWLEDGEMENTS This study would not have been possible without the collaboration of many different individuals and organizations, primarily within Oklahoma. In this page, we wish to acknowledge their important contributions: Karen Frensley, Director of the Oklahoma Transformation Project, provided valuable guidance and direction, as well as political support, throughout this study. From the inception of this study to its completion, we have been guided by the Workforce Study Team, a group of dedicated volunteers who designed the goals of the study, reviewed and commented on methods and findings, and contributed recommendations to this report. The Team was chaired by Nola Harrison of St. Anthony Hospital, who worked closely with us to set agendas for each meeting and to keep us all on task, as well as providing information and advice from her own experience. Other members of the group included Carolyn Archer, David Asetoyer, Sara Barry, Contessa Bass, Donna Woods Bauer, Margaret Bradford, Nichole Burland, Renea Butler-King, Dawn Carson, Sidna Chambers, Jack Chapman, Rita Cooksey, Marva Crawford-Williamson, Richard DeSirey, Hugh Doherty, Jim Durbin, Fred Eilrich, Terrie Fritz, Annette Fulton, Jim Giffin, Chuck Gressler, Amber Guerrero, Marvin Hill, Martha Holmes, Jim Igo, Lydia Johnson, Connie Lake, Tracy Leeper, Alesha Lilly, Randy McCrary, Cathy Olberding, Glenda Owen, Rebecca Pruitt, Sandy Pruitt, Jolene Ring, Cheryl St. Clair, Susie Seymour, Bill Slater, Terry Smith, Debbie Spaeth, Jeff Talent, Ross Tripp, Ashland Viscosi, Richard Wansley, and James Wineinger, We received very generous support from Aldwyn Sappleton of the Oklahoma Department of Commerce and Randy McCrary of the Oklahoma State Regents of Higher Education. They provided key current data, as well as future projections, on the state behavioral health workforce and annual degrees awarded from Oklahoma institutions of higher education respectively. Two organizations volunteered to participate in a pilot study of the three workforce surveys. We are grateful to Nola Harrison and her colleagues at St. Anthony Hospital and Terry Smith and his colleagues at Sequoyah Enterprises, Inc. for their efforts to pre-test the organizational, program, and staff surveys. Robert Powitzky of the Department of Corrections and Alesha Lilly of the Department of Health assisted in obtaining individual data from state employees who provide mental health services under the auspices of their agencies. The directors or commissioners of six Oklahoma state agencies assisted in obtaining the participation of their behavioral healthcare contract providers. The participating directors were: Gene Christian (Office of Juvenile Affairs), Terry Cline (Department of Health), Michael Fogarty (Health Care Authority), Howard Hendrick (Department of Human Services), Justin Jones (Department of 8 Corrections), and Terri White (Department of Mental Health and Substance Abuse Services). Additionally, in her role as the President of the Oklahoma Psychiatric Hospital Association, Nola Harrison, provided assistance in obtaining the participation of her member organizations. Our colleagues Alan Ellis, Joseph Morrissey, and Kathleen Thomas of the University of North Carolina provided estimates of staffing shortages in Oklahoma among psychiatrists and other prescribers of psychiatric medications. Our AHP colleagues Denise Lang, Nick Huntington, and Darby Penney, provided assistance in data collection, database management and analysis, and report editing, respectively, and our ODMHSAS colleague Steve Davis provided comments on our approach to estimating future staffing needs. Kevin Huckshorn (formerly the Director of the NASMHPD Technical Assistance Center) and Jean Carpenter-Williams of the University of Oklahoma provided consultation on workforce competencies of the adult and children’s mental health workforce, respectively. Sheryl McLain, formerly the Executive Director of the Oklahoma Health Care Workforce Center, provided guidance on the development of the workforce survey. Deborah Dennis (Policy Research Associates) and Deb Kupfer (Western Interstate Commission for Higher Education) offered insightful comments and suggestions on an earlier draft of this report. We also thank the many, many individuals working at Oklahoma behavioral healthcare provider organizations who participated in the surveys that provided key data for this report. None of the persons cited above are responsible for any errors we may have made in this report or earlier reports of this study. We are very appreciative for all of the assistance that we received over the course of this study, and we apologize if we inadvertently excluded the names of additional contributors. 9 CHAPTER 1: INTRODUCTION In 2005, Oklahoma was one of seven states (now nine) to receive a five-year Mental Health Transformation State Incentive Grant (TSIG) from the federal Center for Mental Health Services (CMHS). The purpose of this grant was to help transform state mental health systems from “broken and fragmented” systems to systems that deliver excellent mental healthcare with a focus on recovery (President’s New Freedom Commission on Mental Health, 2003). A major challenge faced by all states was assuring a stable, competent workforce available to provide needed services. The Oklahoma Behavioral Healthcare Workforce Survey and associated studies were conducted by Advocates for Human Potential, Inc. (AHP) through a contract with the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS) to assist with evaluation activities related to Oklahoma’s behavioral health transformation initiative. The studies were developed and implemented under the guidance of an advisory group, the Workforce Study Team, which was convened through the Governor’s Transformation Advisory Board (GTAB) Workforce Committee, as part of the Transformation initiative. Purpose and Goals State mental health authorities typically do not have empirical information about the characteristics of their current workforce. In order to fill this information gap, we undertook a number of studies, as well as searched for relevant research, that provided useful information for understanding the difficulties faced by staff providing mental health services in Oklahoma. Taken together, the workforce studies were designed with three broad goals in mind: 1. Respond to interests of GTAB Workforce Committee convened through Oklahoma’s behavioral health transformation initiative. 2. Develop behavioral health complement to information gathered through Oklahoma Healthcare Workforce Center and Oklahoma Hospital Association surveys. 3. Provide information that can be used for provider organization and state agency-level planning and advocacy. The largest of these studies was the Oklahoma Behavioral Healthcare Workforce Survey, a statewide survey that focused on staffing of agencies and programs that provide behavioral healthcare. The survey itself was intended to address six particular goals of the Workforce Study Team and other project stakeholders, including: 1. Estimate rates of recruitment, retention and turnover by position. 2. Determine reasons for leaving, including those related to wages and benefits (e.g., health insurance, schedule/shift, child care). 3. Analyze current representation of adult peers and family members in the workforce. 4. Describe linguistic (and cultural) competency of the workforce. 10 5. Describe capacity of state workforce to address current needs of clients and employers. 6. Describe current access to behavioral healthcare services in primary care settings and identify (types of) professionals delivering such services. Methodology Survey Measures Where possible, survey items were drawn from established measures. The two primary sources of items and item structure were: · Addiction Technology Transfer Center Workforce Survey: A staff and director survey instrument was developed for the Northwest Addiction Technology Transfer Center (see Addiction Technology Transfer Center Network, n.d.) and subsequently adapted for use in at least six other states. Oklahoma workforce survey items that were drawn from or based on this instrument included those relating to recruitment barriers and causes of turnover, organizational strategies for supporting staff development, and distribution of daily responsibility, as well as a number of basic demographic related items. · Federal Human Capital Survey (FHCS): The FHCS is an instrument developed by the U.S. Office of Personnel Management and used to measure employees’ job satisfaction and their perceptions of the degree to which their organization exhibits characteristics consistent with those of successful organizations. The instrument was used to survey federal employees in 2004, 2006, and 2008, with over 200,000 responses received in the 2008 use alone (United States Office of Personnel Management, n.d.). Oklahoma workforce survey items that were drawn from the FHCS include those related to staff work experience and job satisfaction. Additional items were developed and selected for the Oklahoma Behavioral Healthcare Workforce Survey with the guidance of the Workforce Study Team and outside consultation when necessary. Pilot Study The pilot study involved two organizations: a residential care provider which operates congregate care facilities in locations throughout Oklahoma, and an inpatient care provider which operates a variety of behavioral healthcare programs in the Oklahoma City area. Between the two organizations, a total of 28 distinct programs participated in the pilot. These programs provided an array of services designed to respond to a variety of consumer needs and interests. Programs ranged from long-term residential care to acute detoxification, and served children, youth, adults and older adults, and supported people with needs related to mental health, substance abuse and co-occurring disorders. The pilot study took place in June and July, 2008. In August 2008, the preliminary results of the pilot were reviewed with the Workforce Study Team, as was a report of the survey process, including challenges encountered and suggestions offered by pilot participants. Based on these reports and the discussion with the Workforce Study Team, some redundant items were eliminated, the schedule and scope of organizational recruitment was scaled back, and the recruitment material packet was revised. Data from the pilot were included in the larger data analysis of the Oklahoma Behavioral Healthcare Workforce Study. 11 Survey Structure In order to capture the range of information desired by the Workforce Study Team and other project stakeholders, the survey was designed with three components: 1. An organizational survey focusing primarily on organizational accreditation and benefits as well as basic information on organizational structure. Organizational structure information was used to create organization-specific versions of the program manager and staff surveys described below. The organizational survey component was completed by a single member of each participating organization (typically a human resources administrator in larger organizations, or the director in smaller organizations). 2. A program manager survey containing items related to program staffing, vacancy, recruitment barriers, causes of staff turnover, program and staff capacity and training needs. Within each organization, each program manager with unique supervisory responsibilities for one or more behavioral healthcare programs was invited to complete a program manager survey. Occasionally, organizations would indicate that two or more program managers supervised a single program. In these cases, AHP worked with the organization to develop a survey plan to avoid duplication of program manager responses. 3. A staff survey focusing on staff work experience, job satisfaction, education and training as well as demographic characteristics and status as current or prior consumers or family members of consumers. All direct providers of behavioral healthcare services in participating organizations were invited to complete a staff survey. As described in the recruitment subsection below, however, staff recruitment was highly dependent on program manager assistance. Data collection and process were structured so that the three components could be linked. Staff responses could be grouped by program and organization, and linked to the appropriate program data (provided via the program manager survey) and organizational data (provided via the organizational survey). 12 Exhibit 1.1: Survey Structure ORGANIZATIONAL SURVEY PROGRAM MANAGER SURVEY PROGRAM MANAGER SURVEY PROGRAM MANAGER SURVEY STAFF SURVEY STAFF SURVEY STAFF SURVEY Recruitment & Participation Organizations were recruited in industry groups, generally according to state agency funding and oversight. The following nine industry groups were recruited: · Mental Health: Organizations providing primarily mental health services and operated under contract with or by ODMHSAS. · Oklahoma Psychiatric Hospital Association (OPHA): Psychiatric hospitals or hospitals with psychiatric units within OPHA membership. · Oklahoma Department of Human Services (DHS): Organizations providing a range of residential and outpatient services for children, youth and adults with a variety of service needs and operated by or under contract with DHS. · Oklahoma Office of Juvenile Affairs (OJA): Organizations operated by or under contract with OJA, providing services to children and youth in a range of settings. · Substance Abuse: Organizations providing primarily substance abuse services and operated under contract with or by ODMHSAS. · Oklahoma Department of Corrections (DOC): Providers employed by DOC and offering mental health services within correctional facilities across Oklahoma (substance abuse services are contracted out and were therefore not included in the survey). · Other Medicaid: A random sample of organizations that were not included in any of the above groups but that do provide behavioral healthcare services and bill Medicaid. 13 · Federally Qualified Health Centers (FQHC): Organizations that provide behavioral healthcare services and have obtained the FQHC designation. · Child Guidance: Child Guidance clinics operated by the Oklahoma State Department of Health (OSDH). The number of organizations, program managers, and staff members recruited by industry group are shown in Exhibit 1.2 on the next page. When considering the implications of the results described in this section, it may be helpful to bear in mind the degree to which the responses we received can be considered representative of the views of Oklahoma behavioral healthcare agencies, program managers, and staff. Exhibit 1.2 indicates that 63% of invited organizations responded, with participation rates by industry group ranging from 41% to 100%. We can be relatively confident that responses from agencies in high participation industry groups are representative of those industry groups, but less confident of the representativeness of responses of agencies in low participation industry groups. Similarly, among participating organizations, average program manager response rates ranged from 67% to 100%, with an overall average of 72%. Among participating programs, staff response rates ranged from 4% to 100%, with an overall average of 26%. Our confidence in program manager and staff response representativeness should also vary by industry group participation rate. Additionally, within industry groups or within the sample as a whole, we can have more confidence in the representativeness of program manager responses than we can in the representativeness of staff responses. Finally, it is important to note that, as the recruitment process was driven by state agency oversight and funding, any First Nations provider organizations that are not funded or credentialed by one or more of the above state agencies were not recruited. 14 Exhibit 1.2: Participation by Industry Group Organizations Program Managers Direct Care Staff Industry Wave Date Launched Number of Responses Response Rate Number of Responses Response Rate Number of Responses Response Rate1 Mental Health 9/30/08 27 79% 102 67% 443 21% OK Psychiatric Hospitals Association 11/04/08 12 41% 32 74% 363 26% OK Department of Human Services 1/14/09 10 83% 20 74% 150 31% OK Office of Juvenile Affairs 1/14/09 11 79% 12 86% 38 13% Substance Abuse 5/14/09 38 62% 52 74% 234 36% Department of Corrections 8/17/09 12 100% 6 100% 40 63% Other Medicaid Providers 8/19/09 11 48% 9 82% 6 4% Federally Qualified Health Centers 8/19/09 5 45% 2 67% 14 100% Child Guidance Clinics 10/26/09 12 100% 8 89% 37 73% Total: 116 63% 243 72% 1325 26% 1 Staff participation rates are based on programs for which total number of staff is known. 2 The Department of Corrections and Child Guidance Clinics are multiple service sites however due to the nature of the programs they were surveyed as one organization. 15 At the beginning of the recruitment phase for each industry, enrollment packets were mailed to the organizations that had been identified for recruitment. These packets included a cover letter from the relevant state agency administrator, describing the value of the project and encouraging the organization to participate. Following this cover letter were informational sheets from AHP about the purpose of the survey and the enrollment process. A single organizational designee completed the organizational survey component online, providing program manager names and email addresses. Organizations that did not initially respond were encouraged to do so via email, telephone, and U.S. mail reminders, which included sample reports that served as an organizational incentive. Once an organization completed the organizational component of the survey, a unique version of the program manager and staff survey was created to reflect the structure of the organization. Program managers were mailed invitational emails with recruitment letters as attachments to be distributed to staff. Regular reminders were sent to program managers, including counts of staff responses for each program, which were copied to the organizational designee and/or executive director. A variety of additional measures were employed to encourage participation at each stage of the survey. For most industries, personnel from the relevant Oklahoma state agency made additional follow-up calls. Additionally, AHP staff made in-person visits to key organizations to provide assistance in participating in the survey, or to encourage participation. Other Data Sources While the workforce survey is the largest component of this project and is generally the focus of this report, data were drawn from a variety of additional sources: · Economic Modeling Systems Inc (EMSI): The Oklahoma Department of Commerce provided average hourly wage rate norms for a range of behavioral healthcare positions at the national, regional and state level. · University of North Carolina (UNC) Staffing Needs Study: Data were drawn from a UNC study of professional staffing shortages, conducted under contract to the Health Resources and Services Administration (HRSA). · Oklahoma State Regents of Higher Education: The Regents of Higher Education provided information on the number of behavioral healthcare related degrees awarded by category and by year since 2001, as well as information on the number of degrees anticipated to be granted and anticipated to be needed. The data derived from these sources complement the data collected from the survey and provide information on subjects that could not be covered by the survey. In doing so, they allow the project to provide a more comprehensive response to the Workforce Study Team’s interests and goals. 16 CHAPTER 2: STAFF SEPARATIONS Staff separation rate (turnover) is a near-universal concern in behavioral healthcare programs. High separation rates increase program costs, reduce return on investment for staff development, and impact quality of care. Anecdotal evidence of the negative impact of turnover on provider-consumer relationships abounds. Given this, it is not surprising that study stakeholders identified staff separation as a principal area for investigation. Information was gathered on staff separations through both the program manager and staff surveys. Program managers were asked to review a list of 18 possible causes of staff turnover and to indicate which of these were most relevant to their program. Managers were also asked to report on the number of separations in their program using the study’s six primary position categories. Staff members were asked to report whether they intended to leave their position within the next 12 months. This section describes the responses to these survey items, and the relationships between these items and other program, organizational and staff characteristics. Program Manager Perceptions of Causes of Turnover Program managers were asked to identify three causes of staff turnover in their programs. The causes most frequently cited by the responding program managers are shown in Exhibit 2.1.3 Percentages for this item add up to more than 100, as three causes of turnover were selected for each program. Program managers perceive dissatisfaction with salary/pay as the greatest contributor to staff separations; 63% cited dissatisfaction with pay as a significant cause of turnover in their program. Other factors cited by at least one third of the program managers were excessive paperwork (43%), emotional burnout (36%) and excessive on-the-job stress (33%). Exhibit 2.1: Program Manager Perceptions of Causes of Turnover across Industries Data from the program manager surveys. 3Causes cited by less than 10% of program managers are not shown in Exhibit 2.1. These causes were: dissatisfaction with workplace location; dissatisfaction with relationship with supervisor; dissatisfaction with on-call responsibilities; difficulties with transportation; difficulties with child care; dissatisfaction with health insurance; dissatisfaction with time off; concern about on-the-job safety; and dissatisfaction with coworkers. 17 We examined the relationships of the perceived causes of staff turnover to seven key dimensions: industry group (Mental Health, Substance Abuse, Department of Human Services, Office of Juvenile Justice, Oklahoma Psychiatric Hospital Association, Child Guidance, Federally Qualified Health Centers, Other Medicaid and the Department of Corrections)4, region (northwest, southwest, northeast, southeast, Tulsa metro, Oklahoma City metro), service type (mental health, substance abuse, combined mental health and substance abuse, and services for people with developmental disabilities and mental health or substance abuse needs), program setting (inpatient, criminal justice, residential, or outpatient), service population (children, adults, both), organizational type (state vs. private), and organizational size (small, medium, large). The following causes of turnover were significantly different (p<.05) across at least one of the seven dimensions: (1) dissatisfaction with salary/pay (Salary), (2) dissatisfaction with career ladder, (3) excessive paperwork (Paperwork), (4) dissatisfaction with job responsibilities (Responsibilities) and (5) dissatisfaction with shift/work hours (Hours). While none of these causes of turnover varied by region or service type, there was variation across industry group, program setting, service population, organizational size, and organizational operation (state vs. private), also considered a proxy for organizational benefits. Following these findings, logistic regressions were performed to examine the relationship between the dimensions - taken together - and each of the following four causes of turnover: Salary, Responsibilities, Hours and Paperwork. Industry, service population, organizational type, program setting, and organizational size were included in this testing. Tables summarizing the results of these regressions can be found in Appendix A1. Four additional parsimonious logistic regression models can be found in Appendix A1 as well, for a total of eight regression models. In summary, when controlling for other factors, program manager perceptions of causes of staff turnover suggest that: 1. The role of salary/pay in turnover varies by industry. 2. The role of excessive paperwork and dissatisfaction with job responsibilities in turnover varies by service populations. 3. The role of excessive paperwork in turnover also varies by program settings. Pay as a Perceived Cause of Turnover Exhibit 2.2 provides details of the relationships between organizational industry and pay as a perceived cause of turnover.5 Program managers in OJA organizations were most likely to cite pay as a cause of turnover, while those in OPHA organizations were least likely to do so. Specifically, 90% of program managers from the OJA industry group perceived staff dissatisfaction with salary/pay as one of the top reasons for staff separations while program managers from the OPHA industry group were only half as likely to name dissatisfaction with salary/pay. At least 70% of program managers from the Mental Health and DHS industry groups cited salary/pay as a cause of turnover. This relationship was upheld in the regression analyses as well, with industry being a significant predictor of program manager perceptions of pay as a 4 Industry group name and abbreviation: Mental Health (MH), Substance Abuse, Department of Human Services (DHS), Office of Juvenile Justice (OJA), Oklahoma Psychiatric Hospital Association (OPHA), Child Guidance, Federally Qualified Health Centers (FQHC), Other Medicaid (MA) and the Department of Corrections (DOC). 5 Industries with fewer than ten program manager responses were not included in the analysis. 18 significant cause of turnover. Program setting, organizational size, and organizational6 operation were not significant in the logistic regression model. Exhibit 2.2: PM Perceptions of Pay as a Cause of Staff Turnover by Industry MH N=102 DHS N=17 OJA N=10 OPHA N=26 SA N=61 Dissatisfaction with salary/pay 76% 71% 90% 42% 53% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. ♦ FQHC, DOC, Other Medicaid, and Child Guidance industries are not included in the analysis because there were fewer than ten programs in these samples. Excessive Paperwork as a Perceived Cause of Turnover Exhibit 2.3 shows the relationship between program manager perception of excessive paperwork as a cause of staff turnover and program setting. Respondents characterized their program setting as one of the following: inpatient (an acute care mental health unit in a hospital, a unit in a substance abuse detoxification facility, or a residential unit within a hospital), outpatient (a unit in a community mental health center, a day program, a psychiatric rehabilitation program or a Program of Assertive Community Treatment/case management program), residential (a group home or a supported housing program), and correctional/criminal justice (a prison or juvenile detention facility). Excessive paperwork was cited as a cause of separations by 60% of program managers from outpatient facilities, 21% of those managing residential programs, 20% of those managing inpatient units, and 10% of those managing programs in criminal justice facilities. The relationship between program setting and excessive paperwork remained when the effects of other variables were considered. Industry group and excessive paperwork had a strong relationship when that relationship was tested on its own, but it did not remain significant in the regression analysis. Service population (adults, children, or both adults and children) was unrelated to paperwork as a cause of turnover when this relationship was tested alone, but became a significant predictor in the regression analysis (Model 2 of the logistic regressions). Program managers in programs serving children cite excessive paperwork as a cause of turnover more frequently than those serving both children and adults. Exhibit 2.3: PM Perceptions of Paperwork as a Cause of Staff Turnover by Program Setting Inpatient N=30 Outpatient N=119 Residential N=47 Correctional N=10 Excessive paperwork 20% 60% 21% 10% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. Dissatisfaction with Job Responsibilities as a Perceived Cause of Turnover Dissatisfaction with job responsibilities varied by service population (Exhibit 2.4), with program managers supervising programs serving both children and adults being less likely to perceive job 6 Organizational size – programs are the unit of analysis. Program managers were asked to identify the number of full-time staff working in each program they supervised. The number of full-time staff were aggregated for each organization. An organizational response rate was calculated and the total number of staff in each organization was divided by the organizational response rate and multiplied by 100. This yielded the total number of full-time staff in each organization (i.e., total staff) which was then divided into three groups – small, medium and large organizations – based on the overall distribution of the total staff. 19 responsibilities as one of the most important causes of staff turnover than were managers supervising programs that serve only adults or only children. While this relationship may not initially seem meaningful, it could be related to the relationship between service population and program setting. Eighty percent of programs serving both children and adults were categorized as outpatient programs. Compared to program managers in inpatient and residential programs, fewer outpatient program managers cited job responsibilities as a significant cause of turnover in their programs. The relationship between job responsibilities and service population is further supported by Model 3 of the logistic regressions (see Appendix A1 - Factors Influencing Program Manager Perceptions of Staff Dissatisfaction with Job Responsibilities as a Cause of Turnover). Organizational size was not significant in the regression model. Although dissatisfaction with job responsibilities varied by industry, program setting, and service population, these were not significant predictors in the full regression model. Exhibit 2.4: PM Perceptions of Responsibilities as a Cause of Staff Turnover by Service Population Children/Adults Adults Only Children Only Dissatisfaction with job responsibilities 4% 21% 25% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. Program Manager-Reported Separation Rates Program managers were asked to report the current number of full time equivalents (FTEs) budgeted for their program and vacant in their program, as well as the number of staff separations that occurred over the previous 12 months in their program. These items were posed in reference to each of six position categories: aides/techs/other paraprofessionals, professionals primarily holding Masters degrees (counselors/therapists/MSW-level social workers), LPNs, psychiatrists and other physicians, doctoral-level psychologists/DSW-level social workers, and RNs7. Exhibit 2.5 shows the position-specific and total separation rates statewide, and for each of the six geographic regions8. 7 This position category structure was developed based on a review of the state position classification and the U.S. Bureau of Labor Statistics Standard Occupational Code (SOC) system. Appendix A15 shows relevant SOC positions categorized according to this six-position structure. 8 To calculate the separation rate for a given region, the number of separations was totaled across participating programs, and this sum was divided by the number of FTEs budgeted across programs. It is important to note that organizations may not have included providers that are contracted with, rather than employed, in the counts that follow. 20 Exhibit 2.5: Cross-industry Program Manager-Reported Separation Rates by Region Position NE NW OKC SE SW Tulsa Statewide Aide/tech 51% 55% 34% 38% 50% 30% 42% Masters-level professional 28% 26% 26% 27% 8% 27% 25% LPN 32% 29% 40% 50% 33% 10% 36% Psychiatrist/ physician 33% 0% 4% 44% 25% 20% 22% Psychologist 13% 0% 0% 0% 0% NA 7% RN 25% 33% 29% 56% 23% 21% 28% Total 40% 41% 31% 35% 32% 27% 34% Data from the program manager surveys. Calculating Program Separation Rate Percents in the table above were calculated by summing separations and budgeted positions across the region. In the analysis that follows, separations are calculated at the program level. Programs, rather than organizations, were chosen as the unit of analysis due to concerns that program characteristics and local program environment may vary widely within larger organizations - particularly those with programs across a wide geographic range. Program separation rates ranged from 0% to 200%. Separation rates of greater than 100% are possible because positions may turn over more than once within a year. The median separation rate was 25%, meaning that roughly half of the participating programs had a separation rate below 25%, and roughly half had a separation rate above 25%. In other words, at least one out of every four positions turned over in roughly half of the programs surveyed. Appendix A2 gives more information on the distribution of the program separation rates. The initial analysis of the relationships between separation rates and other program variables was attempted with three approaches to handling separation rates: by breaking participating programs first into two groups of equal size, then into three groups of equal size, and finally into four groups of equal size. The approaches yielded fairly similar results, with those for the two group approach being slightly more favorable than those for the alternatives. This approach involves dividing the group at the median of 25%, a rate which is consistent with a high turnover definition used in a recent, related study (Strolin-Goltzman, 2008). Relationships between Separation Rates and Other Program Variables The relationship between separation rate and a number of program characteristics and related variables was examined. Relevant, recent literature was reviewed. The following identifying program characteristics were identified as being potentially related to separation rates: 1. Staff role clarity 2. Staff job satisfaction 3. Staff salary and benefits 4. Staff sense of personal accomplishment 5. Staff age 6. Staff intention to leave 7. Staff job level/experience 21 8. Staff burnout 9. Lack of alternative job options The primary source of information for items 1- 6 is the staff survey. Because of concerns about the representativeness of the staff data, these items were not considered feasible for this analysis. Most of these variables are also established predictors of staff intention to leave, and could therefore be employed in the predictive model of intention to leave (itself the strongest predictor of separation rates, Mor Barak et al., 2001). Staff job level/experience as a program characteristic was measured using the program manager reports of the FTEs budgeted for their programs. As these reports were specific to position type, we were able to create variables reflecting the proportion of each position type within each program’s staffing pattern. Masters-level counselors and techs made up by far the largest proportion of program staff. On average, Masters-level counselors made up 50% of the program staff, and techs made up 39%, a significant finding in and of itself. The remaining four position categories ranged from a high of 6% (RNs) to a low of 1% (PhDs). Appendix A3 offers more information about the distribution of each of the six position type proportions. Staff burnout as a program characteristic was measured by program manager indication that burnout is one of the top three reasons for staff turnover within their program. We also looked for relationships between the other frequently-cited causes of turnover and separation rate. A proxy for lack of alternative job options was created using the program region code: Programs located in the Tulsa and Oklahoma City metro areas were considered to be located in areas with better alternative job options, while those in the remaining, more rural, regions were coded as being located in areas with fewer job options. Finally, relationships between separation rate and each of the study dimensions described earlier (industry, region, service type, program setting, population age, state operation, and organizational size) were examined. Analysis and Results Analysis to identify relationships between separation rate and each of the variables above on an individual basis was performed. Most of these did not prove to be statistically significant: None of the frequently-cited causes of turnover were associated with program separation rate, nor was the job options proxy. Of the staffing and study dimensions variables, proportion of Masters-level counselors, proportion of techs, industry, and state operation were significantly associated with separation rate, as were two approaches at measuring benefits9. Further information about the items and the relationships identified may be found in Appendix A4. Logistic regression was then performed to test the relationships between separation rate and multiple predictor variables. Looking at the relationship between the two staffing variables (proportion of techs and proportion of Masters-level counselors) it was determined that these variables were too closely related to include in the regression model. Details of the analysis used 9 Upon closer inspection, the results for the benefits items were difficult to interpret (i.e., suggesting an inconsistent or nonsensical relationship between separation rate and benefits). These items were discarded. 22 to determine this can be found in Appendix A5. Ultimately, the model included the following program characteristics: proportion of techs, industry10, and state operation. Both proportion of techs and state operation remained significant in the regression model. As shown in Exhibit 2.6, on average techs made up 31% of the staff in low separation programs, while they made up nearly half of the staff in high separation programs. This is consistent with the literature indicating that high staff experience, job level, and pay are associated with lower turnover. Exhibit 2.6: Proportion Techs in Low Separation and High Separation Programs Staff position type predictors Mean proportion low separation programs Mean proportion high separation programs Proportion Techs 31% 48% Data from the program manager surveys. A more detailed look at the relationship between position type and separation is available in Appendix A7. The distribution for programs in state vs. privately operated organizations is also as anticipated. Half of the programs in private organizations fall into the high turnover group, while less than one-third of the programs in state operated organizations do (Exhibit 2.7). It is believed that this relationship is at least in part a result of the better benefits package offered by state operated organizations. Exhibit 2.7: Proportion of Programs in High Separation Group by State/Private Operation Operation (assigned) Private (N=188) State (N=56) Proportion in high turnover group 50% 29% Data from the program manager surveys. While only a proportion of techs and state operation remained significant in the regression model, Appendix A8 offers details on the remaining variables that were tested. Staff Intention to Leave Staff were also asked about their plans to leave their organizations within the next year. Those who reported that they were planning on leaving were asked to indicate whether they planned to retire, find another job within the behavioral healthcare field, find a job outside the field, or pursue some other option. Exhibit 2.8 shows the percentages of program managers and staff reporting each of these plans. 10 While proportion of techs and state operation remained significant in the regression model, industry became insignificant, suggesting that the relationship between industry and separation rate may have been in part due to a relationship between industry and state operation, or possibly between industry and staffing patterns. A detailed look at the results of this model can be found in Appendix A6. 23 Exhibit 2.8: Intention to Leave Frequencies Response (N=1244) % No, don’t intend to leave within a year 80% Yes, to retire 1% Yes, to take another job in behavioral health 7% Yes, to take a job outside behavioral health 4% Yes, other 7% Data from the staff surveys. This variable was recoded into two categories by combining all categories representing any intention to leave (do intend to leave within a year: 19%, and do not intend to leave within a year: 80%) for the analysis that follows. Relationship between Intention to Leave and Staff and Program Variables As with separation rates, predictor variables were chosen following a review of the literature. This review supported the use of the following variables: 1. Staff burnout; 2. Work-life fit; 3. Job satisfaction; 4. Empowerment; 5. Workplace incivility; 6. Staff age; 7. Job level/experience; 8. Professional and job commitment; and 9. Income. Staff burnout was not measured directly by the staff survey. Related items, such as My workplace is too stressful, would have appeared to provide reasonable proxies but more closely matched other predictors examined in and not supported by the literature. The same holds true for workplace incivility and empowerment. The survey did not examine work-life fit or professional/job commitment. The survey’s overall job satisfaction item was chosen as an indicator of job satisfaction. The survey’s staff age variable was transformed into a continuous variable by recoding age categories into midpoints, except for over 64 which was recoded as 69.5, the midpoint between 65 and 74. The survey’s categorical staff income variable was treated in a similar manner, with the following differences: The lowest category (<$10.00/hr) was recoded as the midpoint between $10.00 and $7.25, the minimum wage in Oklahoma. Position types and education level for respondents who checked the upper category ($50.00/hr or more) were examined, and were surprisingly found to be primarily Masters-level therapists, along with a few physicians. For this reason, we used a rate relatively close to the second-highest category, and significantly below one that might be expected for physicians: $62.50. Staff responses to the item How many years 24 have you been in the field? were used to measure staff experience. Detailed information on the distribution of these variables is offered in Appendix A9. Gender and ethnicity were tested using the original dichotomous survey items, and race was tested by collapsing five dichotomous survey items into a single variable with up to six categories: American Indian/Alaskan Native alone, Asian alone, Black/African American alone, Native Hawaiian/Pacific Islander alone, White alone, and more than one race. Due to low Ns, the Asian alone and Native Hawaiian/Pacific Islander alone categories were eliminated from the analysis. In addition to the variables gathered through the staff survey, the relationship of staff intention to leave to key program variables was investigated, including the program manager-cited causes of turnover and the study dimensions described earlier (industry, region, service type, program setting, population age, state operation, and organizational size). Analysis and Results As with the analyses described earlier, relationships were examined between intention to leave and each of the variables described above on an individual basis. As with separation rate, there was no relationship between intention to leave and program manager citation of the significant causes of turnover. Of the study dimensions, only service type and region were significantly related to staff intention to leave. Staff position type, gender, ethnicity and race were not significant, but staff age, experience, pay and job satisfaction were significant. Initially, the relationship between consumer or family status and intention to leave was investigated by collapsing eight dichotomous survey items into a single four-category variable: neither, consumer only, family member only, and both consumer and family member. This variable was significantly related to intention to leave. However, the distribution was difficult to interpret.11 A variety of alternatives were tested, including the eight original survey items.12 Most of these tests did not yield significant results. However, family status did prove to be significantly related to intention to leave, with a higher proportion of family members than non-family members indicating that they planned to leave within the next year.13 11 Staff who identified as being consumers only seemed much less likely to intend to separate than did staff who identified as either family members or both consumers and family members (full details available in Appendix A10). Given this, it seemed possible that the use of this collapsed variable could be obscuring the meaning of the relationship. 12 Eight original survey items include: adult mental health consumer, adult substance abuse consumer, former youth mental health consumer, former youth substance abuse consumer, family member of an adult mental health consumer, family member of an adult substance abuse consumer, family member of a youth mental health consumer, family member of a youth substance abuse consumer. Aggregations of these items across two dimensions individually and together (adult/youth and mental health/substance abuse) were also tested. 13 When family membership was broken down further into mental health and substance abuse, the relationship between being a family member of a mental health consumer and intention to leave was significant, while that between being a family member of a substance abuse consumer and intention to leave was not significant. However, as the latter relationship showed a similar trend (higher intention to leave among family members), the combined mental health and substance abuse variable was retained for further analysis. 25 Logistic regression was employed to determine whether the relationships noted above remained significant when the effects of all variables were considered.14 The model tested included region, service type, population age, job satisfaction, pay, age, experience, and family member status. Of these variables, only job satisfaction and age remained significant. The mean satisfaction score for staff not intending to leave was 1.71, with 1 being very satisfied and 2 being satisfied (Exhibit 2.9). The mean for staff intending to leave was 2.59, closer to 3, or neither satisfied nor dissatisfied. Consistent with literature on the topic, staff intending to leave were younger on average than those not intending to leave (39.67 years versus 43.30 years, respectively). Complete details on the results of the regression model are shown in Appendix A12, and additional details on the relationship of job satisfaction and staff age to intention to leave are shown in Appendix A13. Exhibit 2.9: Satisfaction and Age Among Staff Intending to Stay and Intending to Leave Mean for staff staying Mean for staff leaving Staff overall job satisfaction (N=1241) 1.71 2.59 Staff age (N=1180) 43.30 39.67 Data from the staff surveys. While only these two variables remained significant in the regression model, Appendix A14 gives additional information on the other variables tested. Summary Information related to separations was gathered through program manager reports of the perceived causes of separation in their programs, program managers’ reports of their programs’ separation rate over the previous year, and staff reports of their intention to leave their position within the next year. The most frequently cited barrier was dissatisfaction with pay, which was cited by nearly two thirds of program managers. Excessive paperwork, emotional burnout and excessive on-the-job stress were cited by at least one third of program managers. While program and organization characteristics were related to multiple perceived causes of turnover when the relationships were examined individually, generally only one or two characteristics remained significant in each logistic regression model. Organizational industry was a significant predictor of citing dissatisfaction with pay, with OPHA program managers being the least likely to cite pay as a cause of turnover. Population age and program setting were significant predictors of perceiving paperwork to be a cause of turnover, with program managers in programs serving children citing paperwork more frequently than those serving both children and adults, and program managers in outpatient settings citing paperwork more frequently than program managers in other settings. Service population also related to citation of dissatisfaction with job responsibilities, with program managers from programs serving both children and adults being less likely to cite this as a barrier than program managers from programs serving either adults or children. 14 We began by examining the relationship between staff age, experience, pay, and job satisfaction. While there were some relationships among these variables, none turned out to be strong enough to warrant excluding any of the variables from the regression model. Details of this analysis can be found in Appendix A11. 26 Program separation rates ranged from 0% to 200%, with roughly half of the participating programs having a separation rate below 25% and roughly half having a separation rate above 25%. This median rate was used to divide programs into two categories: low separation and high separation. These categories were related to multiple program and organizational characteristics when the relationships were examined individually, but only two characteristics remained significant in the logistic regression model. High separation programs proved to be less likely to be state operated, and more likely to have a high proportion of techs on staff. On average, techs made up less than one third of the staff in low separation programs, but nearly one half of the staff in high separation programs. These results are consistent with existing literature regarding the relationship between lower staff experience/job level and higher separation rates. The vast majority (80%) of staff did not report intending to leave their positions within the 12 month period following the survey. Intention to leave was related to a range of program, organizational and staff characteristics when the relationships were examined individually, but only two remained significant in the logistic regression model. As would be expected, staff intending to leave reported lower satisfaction with their job overall. Staff age was also related to intention to leave, with the mean age for staff intending to leave being about three and a half years younger than that of staff intending to stay. Both of these findings are consistent with the literature on staff intention to leave. 27 CHAPTER 3: VACANCIES AND STAFF RECRUITMENT BARRIERS Like staff separations, position vacancies are an area of concern in many behavioral healthcare programs. We collected information on position vacancies on two issues: First, program managers were asked to review a list of 19 possible barriers to staff recruitment, and to indicate which of these were most relevant to their program. Second, program managers were asked to report on the current vacancies in their program, using the six position categories described earlier. This section describes the program managers’ responses, and the relationships between these variables and program characteristics. Program Manager Perceptions of Recruitment Barriers Program managers were asked to identify the top barriers to filling staff vacancies in their programs. The barriers cited most frequently are shown in Exhibit 3.1.15 As each program manager was asked to identify three barriers, the percentages for this item add up to more than 100. The most frequently cited barrier was salary/pay, with 57% of program managers identifying this as an obstacle to filling vacancies in their programs. Lack of candidates with desired credentials or work experience, small applicant pool due to geographic location, and competition from other fields were cited as barriers by at least 25% of program managers. Exhibit 3.1: Program Manager Perceptions of Recruitment Barriers Data from the program manager survey. 15 Barriers cited by less than 10% of program managers are not shown in Exhibit 3.1. These barriers are: cumbersome hiring process; career ladder not attractive; childcare not offered; organizational facilities not attractive; organizational reputation; negative stereotypes of service consumers; job responsibilities not attractive; amount of training required; cost of training required; and benefits not attractive. 28 Recruitment Barriers and Program Variables The next four tables illustrate how recruitment barriers varied by industry, region, organizational size and type.16 The following six barriers to recruitment were used in the analysis that follows: 1. Salary/pay not attractive; 2. No candidates with desired credentials; 3. No candidates with desired work experience; 4. Competition from other fields; 5. Problems with funding/not allowed to fill a position; and 6. Shift/work hours not attractive. Industry and Recruitment Barriers All six of the perceived barriers varied by industry. Eighty percent of program managers in the OJA industry group identified salary/pay as one of the most critical barriers to filling vacancies, while only 19% of OPHA industry group program managers cited this as a recruitment barrier. No OJA program managers cited difficulty finding candidates with desired credentials, but roughly two fifths of Mental Health and Substance Abuse industry program managers perceive this to be a recruitment barrier in their programs. Competition from other fields also varied by industry. Program managers working in the OJA industry group were more likely to cite this as a barrier to staff recruitment (70%) than program managers from any other industry group. One third of program managers from the Substance Abuse industry group perceived funding or not being allowed to fill a position to be one of the most pertinent causes of vacancies; only 10% to 15% of program managers from other industries cited this as a barrier to recruitment. Not surprisingly, shift/work hours is more frequently perceived as a barrier by program managers in industries with a high proportion of 24-hour programs (OPHA, OJA). Finally, while nearly one third of OJA program managers perceive the hiring process itself to be a barrier, this process was not cited as a barrier by any Substance Abuse industry program managers. Exhibit 3.2: Program Manager Perceptions of Recruitment Barriers by Industry Perceived Barrier MH N=101 DHS N=17 OJA N=10 OPHA N=26 SA N=61 Salary/pay not attractive 74% 59% 80% 19% 49% No candidates w desired credentials 37% 24% 0% 15% 41% Competition from other fields 32% 12% 70% 31% 15% Funding/not allowed to fill position 14% 12% 10% 15% 33% Shift/work hours not attractive 17% 24% 40% 42% 15% Cumbersome hiring process 11% 18% 30% 15% 0% Data from the program manager surveys. ♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. ♦ FQHC, DOC, Other Medicaid, and Child Guidance industries are not included in the analysis due to the low number of programs responding to these items. 16 Barriers to recruitment did not vary by service type; as a result, service type was not included in the analysis. 29 State Operation and Recruitment Barriers Organizational operation (state vs. private) was related to three barriers to recruitment. As shown in Exhibit 3.3, nearly three-quarters of program managers from state operated organizations cited salary as a barrier, in comparison to just over half of program managers from privately operated organizations. OPHA organizations may be playing a role in this finding: OPHA program managers were significantly less likely to cite salary as a barrier, and OPHA is the only industry group composed entirely of private organizations. Program managers from state operated organizations were also significantly less likely than those from private organizations to cite lack of candidates with desired work experience as a recruitment barrier. Finally, state operated organizations were more likely than privately operated to cite funding as a fundamental obstacle to staff recruitment. Exhibit 3.3: Program Manager Perceptions of Recruitment Barriers by Organizational Type Perceived Barrier State N=53 Private N=181 Salary/pay not attractive 74% 52% No candidates with desired experience 6% 34% Funding/not allowed to fill position 42% 17% Data from the program manager surveys. ♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Organizational Size and Recruitment Barriers Organizational size was associated with program manager perception that salary and lack of staff with desired credentials are recruitment barriers. Program managers affiliated with large organizations (those with an estimated staff size of at least 82 full time employees) cited salary/pay as a reason for staff vacancies more often than those affiliated with small or medium organizations. Further analysis indicated that small organizations (those with an estimated staff size of less than 15 full-time employees) had more professional staff – staff in positions requiring additional education – and were less likely to be inpatient facilities requiring a large number of aides/techs, who typically earn the lowest salary among direct care staff. These differences in staffing patterns may also relate to the finding that program managers in small organizations are the most likely to cite lack of candidates with desired credentials as a barrier to recruitment. Exhibit 3.4: Program Manager Perceptions of Recruitment Barriers by Organizational Size Perceived Barrier Small Orgs N=33 Medium Orgs N=53 Large Orgs N=126 Salary/pay not attractive 42% 45% 67% No candidates with desired credentials 52% 30% 27% Data from the program manager and organizational surveys.♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Region and Recruitment Barriers Finally, geographic region was significantly related to four of the perceived recruitment barriers: absence of candidates with desired work experience, small applicant pool due to geographic location, competition from other fields, and location of agency not attractive. Not surprisingly, 30 two of these barriers are explicitly location-based, and a third (lack of candidates with desired work experience) could also be argued to be intrinsically tied to location or area. As shown in Exhibit 3.5, a small pool of applicants is the greatest barrier to filling vacancies in the northeast and southeast quadrants of the state, while about half of the program managers from the northwest indicated that competition from other fields was a problem with respect to vacancies in the behavioral healthcare field. Exhibit 3.5: Program Manager Perceptions of Recruitment Barriers by Region Perceived Barrier NE N=54 NW N=14 OKC N=74 SE N=32 SW N=30 Tulsa N=26 Statewide N=230 No candidates w desired work experience 15% 21% 30% 19% 40% 46% 29% Small applicant pool due to geographic location 52% 43% 7% 47% 23% 4% 29% Competition from other fields 19% 50% 34% 28% 7% 42% 30% Location of agency not attractive 35% 14% 8% 13% 3% 0% 12% Data from the program manager surveys.♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Salary as a Perceived Recruitment Barrier Given that salary was the most frequently cited recruitment barrier as well as the most frequently cited cause of separations, it warranted further exploration. Logistic regression was used to test the three program variables discussed above (industry, state operation, and organization size) as predictors of salary as a barrier to recruitment. While organization size did not remain significant, both industry and state operation were significant: OPHA program managers were significantly less like than Mental Health industry managers to cite salary as a barrier, and program managers in state operated organizations were significantly more likely to cite salary as a barrier than were those in privately operated organizations. As noted earlier, the significant relationship between salary as a perceived barrier and industry may be attributable to the low proportion of OPHA program managers citing salary as a barrier. Further details on the results of this regression model may be found in Appendix B1. Program Manager-Reported Vacancy Rates As reported in the section on separations, program managers were asked to report the current number of full time equivalents (FTEs) budgeted for their program and vacant in their program. These items were posed in reference to each of six position categories presented in Exhibit 3.6. To calculate the vacancy rate for a given region, the number of vacancies was totaled across participating programs, and this sum was divided by the number of FTEs budgeted across programs. Exhibit 3.6 shows the position-specific and total vacancy rates statewide, and for each of the six geographic regions. It is important to note that organizations may not have included staff that they contract with (rather than employ) in the counts that follow. 31 Exhibit 3.6: Cross-Industry Vacancies by Region Position NE NW OKC SE SW Tulsa Statewide Aide/tech 7% 13% 8% 7% 16% 8% 9% Masters-level professional 15% 9% 12% 11% 2% 36% 15% LPN 4% 14% 9% 20% 33% 0% 9% Psychiatrist/ physician 3% 0% 0% 33% 13% 10% 7% Psychologist 6% 50% 0% 0% 0% NA 7% RN 13% 22% 15% 28% 8% 7% 14% Total 10% 13% 10% 12% 10% 17% 11% Data from the program manager surveys. Calculating Program Vacancy Rate Percents in the table above were calculated by summing vacancies and budgeted positions across the region. In the analysis that follows, vacancies are calculated at the program level.17 Program vacancy rates ranged from 0% to 100%. The median vacancy rate was 4%, meaning that roughly half of the participating programs had a vacancy rate below 4%, and roughly half had a vacancy rate above 4%. Appendix B2 gives more information on the distribution of the program vacancy rates. Relationships between Vacancy Rates and Other Program Variables We examined the relationship between vacancy rate and a number of program characteristics and related variables. Programs were categorized as either having a low vacancy rate (less than 5%) or high vacancy rate (5% or higher). We began by testing for relationships between vacancy rate and each of the frequently-cited recruitment barriers. Then, as with separation rate, we looked for a relationship between staffing patterns (e.g., proportion Masters-level counselors, proportion techs) and vacancy rate. Finally, we looked for relationships between vacancy rate and each of the study dimensions described earlier.18 Analysis and Results We began by performing analysis to identify relationships between vacancy rate and each of the variables above on an individual basis. Only one of the identified variables proved to be related to vacancy rates:19 High vacancy programs had a greater proportion of RNs than low vacancy programs. As shown in Exhibit 3.7 the average proportion RNs for low vacancy programs was 4%, while the average for high vacancy programs was 7%. While this difference may appear relatively small, it was statistically significant. This finding may be related to the comparatively high rate of vacancies among RN positions overall. As noted earlier in Exhibit 3.6 the overall vacancy rate for RN positions was comparable to that for Masters-level counselors, which was 17 As noted in the separation section, programs were chosen as the unit of analysis due to concerns that program characteristics and local program environment may vary widely within larger organizations - particularly those with programs across a wide geographic range. 18 Industry, region, service type, program setting, population age, state operation, and organizational size. 19 None of the frequently cited recruitment barriers were associated with program vacancy rate, nor were any of the study dimension variables. 32 the position type with the highest vacancy rate. Additional information on the (non-significant) findings for the remaining variables may be found in Appendix B3. Exhibit 3.7: Proportion RNs in Low Vacancy and High Vacancy Programs Staff position type predictors Mean proportion low vacancy programs Mean proportion high vacancy programs Proportion RNs 4% 7% Data from the program manager surveys. Summary Information related to vacancies was gathered through program managers’ reports of the perceived recruitment barriers in their programs, and their reports of their programs’ current vacancies. By far the most frequently cited barrier was salary, which was cited by 57% of program managers. Lack of candidates with desired credentials or desired work experience, small applicant pool due to geographic location, and competition from other fields were all cited by more than one quarter of program managers. Program and organization characteristics that were related to multiple perceived barriers included organizational industry, state operation, organizational size, and geographic region. In logistic regression models, salary remained significantly related to industry, with OPHA program managers being less likely to cite salary as a barrier. Likewise, state operation and salary were related, with program managers in state operated organizations more likely to cite salary as a barrier. Program vacancy rates ranged from 0% to 100%, and the median of 4% was used to divide programs into two categories: low vacancy (less than 4%) and high vacancy (greater than 4%). These categories proved to be unrelated to most of the program and organizational characteristic variables. Staffing patterns offered one exception: The mean proportion of RNs in low vacancy programs was slightly but significantly lower than the mean proportion of RNs in high vacancy programs, which could be in part related to the comparatively high rate of vacancies in RN positions, across programs. 33 CHAPTER 4: CURRENT AND FUTURE STAFFING NEEDS The purpose of this chapter is to identify unmet needs for the behavioral healthcare workforce with a focus on type of position. The first section focuses on psychiatrists and other prescribers, primarily advanced practice psychiatric nurses. The second section focuses on other professional and non-professional staff. Each of these sections employs data from different sources so the methods upon which we have relied are described within each section, as well as the implications for higher education. The third section describes one underlying problem, the level of compensation currently available to the Oklahoma workforce. Need For Psychiatrists and Other Prescribers of Psychiatric Medications State mental health authorities typically do not have empirical information about the characteristics of their current workforce. In order to fill this information gap, a number of studies were undertaken, as well as searches for relevant data, that would provide useful information for understanding difficulties faced by staff providing mental health services in Oklahoma. Among the studies that we identified was a study of the relative unmet need for professional mental health workers in the State of Washington (Morrissey, et al, 2007a), undertaken as a part of the Mental Health Transformation State Incentive Grant. Morrissey and his colleagues employed a simple model as the foundation of their work. First, they estimated the number of adults (persons over age 18) who could be classified either as persons with serious mental illness or as persons with other mental health needs. For each of these two types of persons, estimates were developed on the percentages that would access mental health non-inpatient services in one year and the number of units of professional services they would use. Professional services are broken down into those provided by individuals who are licensed to prescribe medications (prescribers) and individuals who are licensed to provide services other than medications (non-prescribers). These estimates then allow new estimates of the numbers of prescribers and non-prescribers needed (in full time equivalents—FTE) to serve a population within a defined geographic area. The estimates of need are then subtracted from the number of licensed professionals available to yield the shortage of professionals. A summary of the model follows: Need = People with serious mental illness + people with other mental health needs Workforce = Prescribers + Non-prescribers Shortage = FTE available – FTE needed It is important to emphasize that these are relative not absolute measures of unmet need. This means that they are most useful in comparing the need from one area to another, but do not necessarily provide an estimate of the exact number of additional professional staff needed. Moreover, apparent surpluses produced by these estimates cannot be relied upon. The study of Washington State was a part of a larger, national study sponsored by the Health Resources Services Administration (HRSA) of the U.S. Department of Health and Human Services. This allowed Morrissey and his colleagues to develop estimates of professional shortages for every county in the U.S. We contacted them and requested estimates for 34 Oklahoma. The findings, as well as the methods employed to arrive at the estimates, are presented here. This also includes some of the limitations of these findings. Findings Most specialty prescribers in Oklahoma are psychiatrists, although there are a handful of advanced practice psychiatric nurses. Other physicians can and do prescribe psychiatric medications, as well. Exhibit 4.1 below presents regional and statewide estimates of counts of prescribers available to provide mental health services in Oklahoma. As previously discussed, the state is divided into six regions, as follows: Central Oklahoma (counties in which Oklahoma City is located) and Tulsa are separately estimated, while.the remaining counties are grouped into four quadrants - northeast, northwest, southeast, and southwest. Counties are grouped because data at an individual county level is often too small to provide reliable estimates. The table shows 278 FTE psychiatrists/prescribers. Exhibit 4.1: Available FTE Mental Health Specialty Prescribers by Licensure Group and by Oklahoma Regions Licensure Region Advanced Practice Psychiatric Nurses (APPN) Psychiatrists Smoothed Total Prescribers20 OKC 10 133 107 Northeast 2 30 78 Northwest 3 8 9 Southeast 3 13 24 Southwest 1 25 38 Tulsa 6 70 32 Total 23 278 287 For psychiatrists, full time equivalents are greater than the raw count because practice pattern data indicate that psychiatrists average more than 40 hours/week. Exhibit 4.2 below presents regional and statewide totals of FTE needed and FTE shortages for prescribers. For the prescriber group, the UNC estimates produce a shortage of 410 FTEs. 20 In the initial analysis, the county is used as the primary geographical unit for shortage estimation. This decision was made primarily due to the lack of accurate small-area data on mental health needs and practice locations, but also because people are likely to travel within larger areas for mental health services. Each county-level need and supply estimate was adjusted using a smoothing method that accounts for travel across county boundaries for mental health services. Within Oklahoma particularly, with its many small counties, ignoring this would lead to overestimates of need. The maximum amount of time that people can be expected to travel for mental health services is about 60 minutes (Fortney, Owen & Clothier, 1999; Fortney, Rost, Zhang et al., 1999). Therefore, for a given index county, the need and supply estimates of counties within a 60-minute radius were weighted and added to the estimates for the index county. The weighted estimates were scaled so that the national need and supply totals for prescribers and non-prescribers were unchanged by the smoothing process. In the final analysis, counties were aggregated by regions within Oklahoma. 35 Exhibit 4.2: Estimates of Shortages of Specialty Behavioral Health Prescribers by Oklahoma Region Region Total FTE Available, Smoothed FTE Needed, Primary Care Adjusted, Smoothed Relative Shortage (FTE), Primary Care Adjusted, Smoothed OKC 107 187 -80 Northeast 78 217 -139 Northwest 9 32 -23 Southeast 24 107 -83 Southwest 38 94 -56 Tulsa 32 61 -29 Total 287 697 -410 Methods Methods are described in detail in Morrissey et al (2007b). Exhibit 4.3 below presents a brief summary of the data sources employed and how the estimates were derived. Exhibit 4.3: Data Sources Employed To Estimate Behavioral Health Workforce Needs and Available Workforce Variable Estimated Source of Data Oklahoma specific data Prevalence of Mental Illness (persons-in-need) National Comorbidity Survey Replication (NCSR); Medical Expenditures Panel Survey (MEPS) Yes Estimates of percent of persons-in-need using mental health services annually MEPS for non-SMI population; Assume 100% for SMI population No Estimates of average units of outpatient services used per person annually NCSR, MEPS No Estimates of visit hours per working day for prescribers Substance Abuse and Mental Health Services Administration (SAMHSA) No Estimates of need met by primary care providers Need estimate reduced by 15 percent in counties without a shortage of primary care providers (no single reference) Yes Estimates of supply of mental health professionals Various sources, generally relevant professional associations Yes Adjustments of need in rural counties that are close to larger counties Various references; assumed maximum travel time would be 60 minutes for mental health services Yes As shown above, Morrissey and his colleagues relied upon a number of data sources in order to estimate each of the variables required to determine workforce shortages. These sources are generally recognized as the most reliable sources of information available, although in several cases these may be the only sources available. 36 Discussion Prior to the completion of the work by Morrissey and his colleagues, the only available estimates of need for mental health professional services were on the website of the Health Resources and Services Administration. However, there was no explanation of the method employed to develop these estimates or references to underlying research. Thus the work described here represents the first systematic attempt to provide appropriate estimates of workforce needs and shortages. Nonetheless, there are limitations that must be recognized. Morrissey et al (2007b) acknowledge that the populations included do not extend to adults who are homeless or in institutions (e.g., inpatient, corrections) or children and adolescents. They also do not include needs for staffing of substance abuse programs. They indicate that the measure of shortage “is probably most useful when taken as an expression of relative, rather than absolute unmet need.” Finally we reviewed the UNC estimates of available FTE with more recent data from Oklahoma State licensing boards. The UNC estimates are generally close, but underestimate the size of the current, licensed workforce. However, there is no data available on whether individuals who are licensed are actually engaged in clinical practice. We know anecdotally that at least some may be retired or only have a part-time practice or are working in administrative, rather than clinical positions. As we have also pointed out above, other licensed individuals are working in positions that are not counted in the need estimates (e.g., agencies serving children and youth, agencies providing adult or child inpatient care). As a result, we believe that the strategy of simply counting licensed practitioners leads to a systematic overestimate of the available supply of such professionals and, therefore, an underestimate of the shortage of prescribers. Addressing the Shortage of Prescribers Oklahoma has three psychiatric residency programs which collectively produce about 13 new psychiatrists per year. Assuming that our estimate of the current need for over 400 prescribers of psychiatric medications is reasonably accurate, it would take over 30 years for these programs to fill the unmet need. This does not account for retirements during this period which will only increase the unmet need. It is unlikely that these residency programs will expand substantially or that psychiatrists will be recruited in significant numbers from elsewhere in the United States because this is a national problem. The numbers of new doctors entering psychiatric residency programs has been falling for over 20 years, and changes that would reverse this trend do not seem likely. Information about Doctors of Osteopathy (D.O.s) either training to practice psychiatry or trained to do so in Oklahoma suggests that this group also is not likely to expand the numbers of prescribers in the foreseeable future. There are no osteopathic residency training programs in psychiatry in Oklahoma, and only a few D.O.s practice primarily psychiatry in Oklahoma (36) and even fewer are certified to do so (18). Given that psychiatrists cannot be expected to fill the need for new prescribers, what options exist? At least three possibilities exist: · Advanced practice psychiatric nurses can be trained to fill this need. At present there are only a handful of persons with this training in Oklahoma, but nursing schools could be encouraged to offer the necessary education. 37 · With an expansion of integrated primary care and behavioral health care, primary care physicians could become an expanded source of prescribers. Creating incentives to develop integrated care practices, targeting training in integrated care, and promoting continuing education in prescribing psychiatric medications, could contribute to an expansion in the numbers of competent prescribers. · Licensing Ph.D. psychologists with special additional training to prescribe medications would also expand the numbers of prescribers; two states now allow this. All three approaches may be necessary to fill the gap, which is quite substantial. If no action is taken to increase the numbers of prescribers, the problem may become worse with the retirements of older psychiatrists, who were trained in an era when psychiatry was a more attractive field. The numbers of retirements may exceed the small numbers of annual replacements. Conclusions The UNC data demonstrate an unequivocal need for more prescribing professionals in all areas of the state. The total estimate of need for 410 additional prescribers is probably an underestimate for reasons discussed above. The area of the State with the greatest unmet need is the northeast quadrant, excluding Tulsa which has the smallest, relative unmet need. Non-Prescribers Exhibit 4.4 below shows current staffing by position type for the state of Oklahoma and the six regions within the State. Then, exhibit 4.5 below shows population-based rates for behavioral healthcare positions by type in Oklahoma and the surrounding states. Oklahoma and the surrounding states are similar in most categories. The major exception is RNs which are less available in Oklahoma. LPNs and MH/SA Techs are marginally more available. There is considerable variation in the availability of behavioral health care jobs within Oklahoma. The central region (OKC) has among the highest rates of availability for all categories of positions. Tulsa is close and leads in availability of MH/SA Techs. The more rural areas of the state have significantly less availability of professionals, psychologists, MH/SA Counselors, and RNs. 38 Exhibit 4.4: Current (2008) Numbers of Behavioral Healthcare Positions by Positions Type in Oklahoma ODMHSAS Region Counts Position State Tulsa OKC NE NW SE SW Psychologist 1,339 195 628 236 58 96 126 MH/SA Counselor 6,993 1,100 2,691 1,485 281 785 651 RN 26,157 5,714 10,839 3,560 962 2,766 2,316 LPN 13,463 2,411 4,163 2,062 716 2,100 2,011 MH/SA Tech 38,590 9,124 11,394 6,474 1,833 5,530 4,235 Exhibit 4.5: Current (2008) Rates per 10,000 Population of Behavioral Healthcare Positions by Position Type in Oklahoma and Surrounding States ODMHSAS Region Rates Position Okla-homa Rate Multi- State Regional Rate* National Rate Tulsa OKC NE NW SE SW Psychologist 3.9 8.1 9.6 3.5 6.3 3.1 2.9 2.1 2.8 MH/SA Counselor 20.3 27.0 34.4 19.5 27.2 19.2 14.0 16.8 14.2 RN 75.8 87.0 92.7 101.4 109.5 46.1 47.8 59.4 50.6 LPN 39.0 35.0 27.5 42.8 42.1 26.7 35.6 45.1 43.9 MH/SA Tech 111.8 121.3 126.1 162.0 115.1 83.8 91.1 118.7 92.5 Regional rate includes the following states: Arkansas, Colorado, Kansas, Louisiana, Missouri, New Mexico, Oklahoma and Texas. Exhibit 4.6 below shows the current (as of 2008) numbers of behavioral healthcare staff by position type for Oklahoma and for the United States overall. It also shows the projected needs for staffing as of 2018. Projections are based principally upon projected population changes, and to a lesser degree on additional factors, described in the Job Growth section below. The additional positions are necessary to maintain the same rates of services currently provided. 39 Exhibit 4.6: Oklahoma and National Current (2008) and Projected (2018) Rates of Change for Behavioral Healthcare Staffing Positions Oklahoma National Position 2008 Jobs 2018 Jobs Change % Change % Change Psychologist 2,738 3,099 361 13.2% 15.8% MH/SA Counselors 9,726 11,377 1,651 17.0% 17.8% RNs 26,552 32,271 5,719 21.5% 22.3% LPNs 13,936 15,554 1,618 11.6% 12.9% Aides/Techs 44,546 54,536 9,990 22.4% 23.8% Total 97,498 116,837 19,339 19.8% 21.3% The difficulty is that these estimates include only job growth in predicting the numbers of new persons needed to fill available positions. However, current members of the behavioral healthcare workforce will be leaving their positions, either for retirement or other reasons. This also needs to be accounted for in developing estimates of persons needed to fill positions annually. Next, an approach employing appropriate data to reach this goal is outlined. Job Growth The table above from the Department of Commerce shows that in 2018 there will be 361 more jobs for psychologists than there were in 2008. One implication is that it is necessary to train or import 361 new psychologists into the system between 2008 and 2018. The source of these estimates is data from Economic Modeling Systems, Inc (EMSI). EMSI uses several different databases to come up with their estimates, including population projections from the Census Bureau. However, that is not the only factor, current employment trends and participation rates from the Bureau of Labor Statistics (BLS) go into the projections. Also included are Internal Revenue Services income and migration data that shed more light on the single employers or those that do not pay into Unemployment Insurance. The simple explanation is that it uses current employment trends by industry and certain population trends. Industry trends, legislation, and several other factors are also used to decipher which industries will be growing. Population is a key component but labor participation, county wages, migration patterns and trading patterns are also factors that influence the model. EMSI breaks down these trends to the county level, which can then be aggregated to the state level. The next table shows the rate of growth for ten years, which translates into an annual growth rate between one and two percent, depending upon the position type. 40 Exhibit 4.7: Ten Year Growth by Position Category Position Category State 2008 Jobs 2018 Jobs % Change Psychologist 2,738 3,099 13.2% Mental Health or Substance Abuse Counselors 9,726 11,377 17.0% Registered Nurses 26,552 32,271 21.5% Licensed Practical Nurses 13,936 15,554 11.6% Mental Health or Substance Abuse Aides/Assistants/ Technicians 44,546 54,536 22.4% Overall Total 97,498 116,837 19.8% This assumes that the persons occupying these positions in 2008 continue to be available to fill positions in 2018. However, this is not the case. Some people who occupy positions in 2008 will retire or leave the field for other reasons over the next ten years. For example, it will be necessary to train or import more than 361 new psychologists into the system for these reasons. The problem is to estimate how many more psychologists will be needed to fill available positions. Accounting For Persons Leaving the Field Let us assume that from 2008 to 2009 the growth rate for psychologists is two percent. Then the change in jobs would be an increase of 55, meaning that there would be a need for a minimum of 55 new psychologists to fill those jobs. In addition, let us assume that there is a 20 percent separation rate among psychologists during 2008 or 550 persons and further that ten percent of those separated actually leave the field. That would mean an additional 55 new psychologist would be needed to fill those vacated jobs, giving a total of 110 psychologists needed to fill the new jobs and the jobs vacated by those leaving he field. If this reasoning is correct, then modeling the number of new persons needed to fill psychology jobs requires an annual estimate of the percent of persons leaving the field. As a part of our survey work, we collected information from 1,349 individual staff who are currently in behavioral healthcare positions. We asked each of those individuals to indicate whether they planned to stay in their position during the next year. Twenty one percent of staff and six percent of program managers indicated that they plan to leave their positions. The percentages of persons indicating that they planned to retire or indicating that they planned to leave behavioral healthcare are shown in the following table. 41 Exhibit 4.8: Staff Planned Separation Rates and Program Manager Estimated Separation Rates (1) (2) (3) (4) (5) (6) (7) Position Type Persons answering survey Percent planning on separating within the next year Percent planning to retire Percent planning to leave the field Sum of columns (4) and (5) Estimated separation rates from program manager surveys Aide/tech 343 21% 0% 6% 6% 42% Masters-level professional 317 19% 1% 3% 4% 26% LPN 37 24% 3% 8% 11% 35% Psychiatrist/ physician21 - - - - - 23% Psychologist 28 21% 4% 0% 4% 11% RN 149 19% 1% 5% 6% 26% Total 874 20%22 1% 4% 5% 35% Note that, with the exception of psychologists, program manager-reported, actual separation rates exceed staff self-reported plans to separate by a wide margin. However, these data can be considered together to estimate a range of possible industry departure rates. At the conservative end of the range is the staff self-report: An overall rate of 5%, with position-type specific rates ranging from 4% to 11% is probably a conservative estimate of rates of person who separate leaving the field. Alternatively, the proportion of planned industry departures can be applied to the program manager-reported separation rates for a less conservative estimate. These range from a low of 11% for psychologists to a high of 42% for the aide/tech positions. These rates are higher than the annual growth rates projected by EMSI. This means that the growth in estimates of persons needed to fill positions year by year will be influenced to a much greater degree by estimates of staff turnover, representing the need to replace existing members of the workforce. There is one additional consideration in estimating the numbers of new persons needed to fill positions. The “jobs” in the EMSI estimates are only filled jobs; they do not include unfilled jobs. Thus, there is a need to take into account vacancy rates for the appropriate position type. The estimates of vacancy rates for Oklahoma for each position type are shown in Table 4.9. 21 There is insufficient data for psychiatrists to provide these estimates. 22 Note that this is very slightly lower than the rate cited in the text above (21%). The information in this table is based only on responses that could be linked to a position type (N=877), while the overall number cited in the text is based on all the responses to this item that were received (N=965). 42 Exhibit 4.9: Vacancy Rate by Position Category Position Type Percent Vacant Aide/tech 9% Masters-level professional 17% LPN 10% Psychiatrist/ physician 10% Psychologist 6% RN 14% Total 12% With this term included, for each position type the number of new persons needed to fill existing vacancies, positions vacated by persons leaving the field and by growth (from EMSI) would be given by the following equation: [new persons needed in year i+1] = [number of jobs in year i] x [growth rate + percent leaving field] x [1- vacancy rate] Findings Findings are presented for three position types: psychologists, mental health and/or substance abuse counselors, and mental health and/or substance abuse aides/techs. The latter are direct care positions that do not necessarily require professional degrees or licensure. We have not included registered nurses and licensed practical nurses because only a relatively small proportion of these positions are in behavioral healthcare and separate estimates of need have been developed by the Oklahoma Healthcare Workforce Center. The need for psychiatrists and other prescribers is discussed earlier in this section. The “net growth” figures in the column to the right show the numbers of additional persons who must either be trained or imported over a ten year period to be sure that the estimated behavioral healthcare positions for 2018 and in the intervening years will be filled. This is 1,808 psychologists, 7,045 mental health and substance abuse counselors, and 51,625 aides or techs. Exhibit 4.10: Net Growth by Position Category State Position Category 2008 Jobs 2018 Jobs 2018 Persons Needed Net Growth Psychologists 2,738 3,099 4,546 1,808 Mental Health or Substance Abuse Counselors 9,726 11,377 16,771 7,045 Mental Health or Substance Abuse Aides/Assistants/ Technicians 44,546 54,536 96,171 51,625 43 Estimates shown in the table above are conservative for the following reasons: · Separation rates are estimated from individual reports of intentions to leave their present positions, rather than the separation rates estimated from program manager reports of persons leaving their positions. The latter are two to four times higher than the former. · The number of positions only includes those who are considered “state covered.” If all positions including persons in individual or small group private practices are included the numbers would also be higher. This is particularly true for psychologists who are much more present outside the public sector than inside. Exhibit 4.11 below shows the numbers of degrees awarded each year over a six year period beginning in 2001-02 and ending in 2006-07. (A detailed breakdown of degrees awarded in specific fields within each of these larger categories is provided in Appendix A15.) With the exception of psychologists, there has been an increase in each category over this time period. The two columns at the right of the table show the number of degrees expected to be awarded cumulatively from 2007-08 through 2017-18 and the need for new degree recipients to meet the demand for new staff positions. The projections of degrees awarded are based upon a simple linear trend model employing the six years of recent data available on degrees awarded. The model may be underestimating the number of psychology degrees to be awarded, in particular. Exhibit 4.11: Degrees Awarded By Public Higher Education Institutions 2001-02 to 2006-07 and Projected to 2017-18 Compared to Projected Need Position Category 2001-02 Degrees 2002-03 Degrees 2003-04 Degrees 2004-05 Degrees 2005-06 Degrees 2006-07 Degrees Cumulative Degrees Projected through 2017-18 Cumulative Projected New Need by 201823 Psychologist 50 64 51 41 44 44 204 1,808 Mental Health or Substance Abuse Counselors 374 375 391 409 360 421 4,478 7,045 Mental Health or Substance Abuse Aides/Assistants/ Technicians24 1,122 1,090 1,129 1,203 1,208 1,262 14,913 51,625 The number of new cumulative degrees projected by 2017-18 consistently falls short of the cumulative projected new need of persons by 2018, as calculated in Table 4.10. This is further exacerbated by the fact that Higher Education data demonstrates that five years after graduating from Oklahoma with a behavioral health degree only 49% are employed in Oklahoma within a behavioral health care field, although the number of persons qualified in these fields that enter into Oklahoma in a given year is unknown. 23 This estimate does not include individuals needed to replace persons in existing positions who retire or leave the behavioral healthcare system. 24 For these positions, we counted individuals with bachelor’s degrees in social science fields. 44 Conclusions The difficulties experienced by program managers of behavioral healthcare services in recruiting staff to fill vacancies are expected to become more complicated in the coming years. For both professional and non-professional staff the numbers of new persons being trained to account for both persons leaving existing positions and the limited expansion anticipated are not keeping pace with the need, even based on conservative estimates. Compensation Earlier in this report, survey data were presented on the reasons why programs have high staff turnover rates and difficulties recruiting new staff. Across all position types and across almost all industry groups the single leading explanation is low salaries. Further survey data were presented from individual staff on their salaries. A second source of data was utilized on staff salaries by position type, as well as national comparisons, comparisons to surrounding states, and comparisons within areas of Oklahoma. The source of these data is EMSI. Findings Findings are presented for five position types, psychologists, mental health and/or substance abuse counselors, registered nurses, licensed practical nurses, and mental health and/or substance abuse techs. The latter are direct care positions that do not require professional degrees or licensure. Data for psychiatrists is not separately available in the EMSI data set. Data are further presented for the state of Oklahoma overall and for six regions within the state. Exhibits 4.12 and 4.13 below present comparisons of wages. For all positions wage rates for Oklahoma are consistently below both national and regional averages. However, the disparity between Oklahoma and national wage rates is larger than the disparity with regional wage rates. Within Oklahoma, there is also variation among the six regions. In general, wages are among the highest in the Tulsa area for all position types except psychologists. The Central (Oklahoma City) region also tends to have higher rates than the other regions. Among the four regions with rural counties, there is no region that is consistently among the highest or the lowest. For two position types there is considerable regional variation. Psychologists range from a high of $31.72 per hour in the Southeast region to a low of $23.66 in Tulsa, a difference of 25%. MH/SA Counselors range from a high of $19.28 per hour in Tulsa to a low of $13.61 in the northwest, a difference of 33%. All other variations are less than 15%. 45 Exhibit 4.12: Comparison of Average Hourly Wage: National, Regional and Oklahoma Norms for Behavioral Healthcare Positions by Type *Regional rate includes the following states: Arkansas, Colorado, Kansas, Louisiana, Missouri, New Mexico, Oklahoma and Texas. Exhibit 4.13: Comparison of Average Hourly Wage: Oklahoma Norms for Behavioral Healthcare Positions by Type ODMHSAS Region Rates Position State Rate Tulsa OKC NE NW SE SW Psychologists $25.74 $23.66* $29.03 $30.47 $25.23* $31.72 $24.56* MH/SA Counselors $15.12 $19.28 $18.36 $15.67 $13.61* $15.57* $16.10* RNs $24.52 $25.25 $24.96 $22.39 $23.63 $21.34 $24.45 LPNs $15.55 $16.48 $16.06 $14.09 $14.73 $13.70 $14.58 MH/SA Techs $12.35 $12.74* $13.01 $11.37 $11.87* $11.64 $11.97 *Rates may vary due to missing values. Summary It is clear that salary rates for all positions are lower in Oklahoma than in the nation and further that Oklahomans filling these positions providing behavioral healthcare are paid less than individuals in all of the surrounding states. There is also some variation within the State. For the two position types that have the largest numbers of persons providing behavioral healthcare, MH/SA Counselors and MH/SA Techs, salaries are higher in the Oklahoma City and Tulsa areas than they are in the more rural northeast, northwest, southeast, and southwest quadrants of the state. Overview of Current and Future Needs for Behavioral Healthcare Workforce As indicated elsewhere in this report, behavioral healthcare programs have difficulty retaining and recruiting staff. There is a very large gap in the need for psychiatrists and other prescribers. Currently, it is estimated that there is a need for 697 prescribers and only 287 professionals available to meet the need, a difference of 410. While the unmet needs for other professionals and non-professionals are not as large proportionately, there are gaps in these position types as well. Additionally, the rates at which institutions of higher education in Oklahoma are producing new graduates with appropriate training are not sufficient to meet these needs, particularly with projected future growth of these positions. Furthermore, attracting new individuals into service Position State Rate National Rate Multi-State Regional Rate* Psychologists $25.74 $30.27 $26.76 MH/SA Counselors $15.12 $18.63 $16.43 RNs $24.52 $30.06 $26.98 LPNs $15.55 $19.51 $17.53 MH/SA Techs $12.35 $14.02 $12.94 46 or training is significantly handicapped by the fact that salaries for both professional and nonprofessional positions in Oklahoma are consistently lower than the surrounding states and the nation, as a whole. 47 CHAPTER 5: BENEFITS & COMPENSATION Information on benefits and eligibility practices was collected via the organizational survey. Organizations were categorized as either state operated or private. Given the commonly-held perception that the state benefit package is preferable to benefit packages for employees of private organizations, it is useful to compare benefit packages offered by these two types of organizations. Benefits Provided Organizations provided information on the types of benefits they offer employees, and on the specifics of their healthcare coverage. Exhibit 5.1 shows the percentages of state operated and private organizations offering each type of benefits. Seventeen state operated and 97 private organizations responded to these items. Exhibit 5.1: Proportion of Organizations Providing Benefits* Data from the organizational surveys. The commonly held belief that state employers offer more comprehensive benefits packages than private employers is supported by the data. All state operated organizations offer full-time employees health, life, dental and disability insurance, a flexible spending account and a wellness program. Health insurance is offered by almost as many privately operated organizations (95%) as state operated, but coverage decreases with each benefit thereafter (life insurance provided by 85%; dental insurance provided by 75%, and disability insurance provided by 62%). 48 On the organizational survey, a representative from each organization was asked to provide the percentage of their employees covered by insurance, as well as the percentage of full time employees’ insurance costs covered by the organization. Exhibit 5.2 shows the average of the 17 state operated and 87 private organizations’ responses to these items. Nearly all state workers were reported to be insured, while just under three quarters of staff working for privately operated organizations had insurance. State operated organizations reported covering all insurance costs for their employees, while privately operated organizations covered an average of 84% of the cost of their employees’ insurance. Exhibit 5.2: Proportion of Staff Receiving Health Insurance and Proportion Costs Covered Data from the organizational surveys. Staff Satisfaction with Benefits Staff were asked to respond to a series of questions about their satisfaction with the benefits their organization offered. For each item, staff were asked to choose one of six responses: very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, very dissatisfied, or no basis to judge. Exhibit 5.3 displays staff responses to questions regarding different benefit types; 1,178 staff responded to at least one of these items. For the purposes of analysis, staff responding that they were either very satisfied or satisfied with a particular benefit were considered to be satisfied. Overall, nearly three quarters of staff surveyed reported being satisfied with the paid leave and paid vacation time they receive. There is less satisfaction with retirement benefits (51%) and health insurance (56%), and the least satisfaction is associated with options for Flexible 49 Spending Accounts (FSAs; 45%) and child care subsidies/other child care options (25%). As noted earlier in this section, both state and privately operated organizations are very unlikely to offer staff child care options. This could ultimately take a toll on the work-family life of employees and impact their overall job satisfaction. Exhibit 5.3: Staff Satisfaction with Benefits Data from the staff surveys. Industry Group and Satisfaction with Benefits Staff satisfaction with benefits varied by industry group and organization type (state operated vs. privately operated). The Child Guidance and DOC industry groups were composed entirely of state operated organizations that typically offer better benefits packages compared to private organizations. Therefore, it is not surprising that staff from these two industries were more likely to be satisfied with the FSAs, health insurance, paid leave for illness/family care, and paid vacation time offered at their organizations. Satisfaction with child care benefits did not vary significantly by industry group, and therefore does not appear in Exhibit 5.4. 50 Exhibit 5.4: Staff Satisfaction with Benefits by Industry Satisfaction with… MH OPHA OJA DOC SA ChildG Health insurance (N=1024) 61% 53% 69% 81% 63% 76% Flexible Spending Account (N=838) 51% 42% 44% 61% 38% 79% Retirement benefits (N=1026) 61% 55% 63% 57% 43% 78% Paid vacation time (N=1035) 75% 69% 86% 95% 80% 95% Paid leave (N=1023) 74% 66% 89% 91% 79% 95% Data are significant at the p<.05 level. Health Insurance Coverage and Satisfaction with Benefits For the purposes of examining the relationship between health insurance coverage and staff satisfaction with benefits, organizations were grouped according to the proportion of health insurance costs covered. As shown in Exhibit 5.5, staff employed by organizations in the high coverage (100% of health insurance costs covered) were more satisfied with their benefits than staff employed by organizations in either of the other two groups. One exception to this finding was satisfaction with child care options, which was similar among staff from the high coverage and medium coverage (80-90% of costs covered) groups. Exhibit 5.5: Percentage of Health Insurance Covered by Staff Satisfaction with Benefits Satisfaction with… Employer Covers <78% N=115 Employer Covers 80-90% N=102 Employer Covers 100% N=228 Child care options 11% 27% 25% Flexible Spending Account (FSA) 34% 46% 53% Health insurance 36% 68% 73% Retirement benefits 30% 62% 65% Paid leave for illness & family care 66% 77% 87% Paid vacation Time 71% 78% 85% Data from the staff and organizational surveys. ♦ Data are significant at the p<.05 level. ♦ Staff is the unit of analysis. Staff Pay As discussed in the separations section, staff were asked to indicate their hourly wage range using a multiple choice question with $5.00 per hour increment pay ranges beginning at less than $10.00 per hour and ending at $50.00 or more per hour. For the purposes of analysis, the responses to these items were transformed into scale data using the midpoints of the pay increments. Details on the overall distribution of this variable are shown in Appendix A9. Exhibit 5.6 shows the distribution of the original pay categories. As might be anticipated, the responses were heavily clustered in the more modest pay categories. Over half of responding staff earned less than $15.00 per hour, with close to one-fifth making less than $10.00 per hour. Oklahoma uses the federal minimum wage, which increased from 51 $6.55 to $7.25 per hour during the data collection period for this study. Given that ninety-two percent of staff reporting wages of less than $10.00 per hour also reported being employed full-time, the minimum wage rate and upper limit of this wage category can be used to create an estimated gross annual income range of $14,500.00 to $20,000.00 for the majority of staff in this category (those employed full-time). Staff earning towards the upper end of the range are at 185% of the 2009/2010 poverty guidelines if they have no dependents, but are under the poverty line if they have more than two dependents. Staff earning towards the lower end of the range are at roughly 133% of the 2009/2010 poverty guidelines if they have no dependents, but are under the poverty line if they have any dependents (Office of the Assistant Secretary for Planning and Evaluation, 2010). Exhibit 5.6 Staff Pay Distribution Staff pay varied by position type, as expected. Exhibit 5.7 shows the mean hourly wage as well as the lowest pay range and highest pay range selected for staff in five position categories. Physicians are not included in this table due to the small number of physicians responding to the staff survey. Psychologists reported the highest hourly wages, but those reported by Registered Nurses were fairly similar. Licensed Practical Nurses had a mean hourly rate relatively close to that of counselors, most of whom had Masters of Social Work or other Masters degrees. Techs reported the lowest wages, with an average of $11.23 per hour. 52 Exhibit 5.7: Wage by Position Type Mean Hourly Wage Lowest Wage Reported Highest Wage Reported Aide/tech (N=385) $11.23 Less than $10.00 $20.00 - $24.99 Masters-level professional (N=469) $18.64 Less than $10.00 $50.00 or more LPN (N=40) $16.38 $10.00 - $14.99 $20.00 - $24.99 Psychologist (N=12) $28.33 $15.00 - $19.99 $35.00 - $39.99 RN (N=124) $26.71 Less than $10.00 $40.00 - $44.99 Overall (N=1003) $17.03 Less than $10.00 $50.00 or more Mean wages based on midpoint of pay range selected ♦ Data from the staff survey. Relationship between Pay and Program and Staff Variables The relationship between staff pay and a variety of program characteristics and staff variables was examined. First, it was determined whether there were relationships between staff pay and the study dimensions variables described in earlier sections.25 Then relevant staff variables were considered: staff member race (American Indian/Alaskan Native, Asian, Black, Native Hawaiian/Pacific Islander, White and more than one race), ethnicity (Hispanic/Non-Hispanic), gender, age, current position tenure, organizational tenure, years in behavioral healthcare field, highest degree achieved (high school/GED, Associates/two-year degree, Bachelors/four-year degree, Masters degree, doctoral degree, and medical degree) and current position title (reported using the study’s six position-type scheme, described earlier). Tenure items were reported in years.26 Analysis and Results The relationships between staff pay and each of the variables above were tested in a linear regression model. 27 Of the program variables, service type, consumer population age, and organizational size remained significant in the regression; staff position was the only staff 25 Study dimensions variables - industry group, geographic region, program service type, service setting, age range of consumer population, state operated status, and organizational size. 26 As noted in the separations section, age was reported using age range categories, but for the purposes of analysis, the midpoint of these ranges was used. Given the very small number of physicians responding to the survey, this position type category was eliminated from the analysis. 27 A number of variables were excluded due to concerns about correlation with other predictors. These included industry group, years in position, years in field, and education. As both members of correlated variable pairs were generally strongly associated with pay, the exclusion decisions were based on the perceived utility of the variables. Additionally, two variables were excluded because their association with pay, while significant, was unexpected and difficult to interpret as anything other than the result of a relationship with another predictor variable. First, while the Oklahoma City metro area’s position as the region with the highest mean hourly wage ($19.08) was not surprising, the Tulsa metro area’s mean wage was unexpectedly much lower ($16.86) and was also much lower than that for the southeast quadrant ($18.57). We believe this is at least partially a result of the relationship between region and industry. Additionally, the southeast quadrant’s unexpectedly high average wage may be related to the small but still disproportionately high number of doctoral-level clinicians reporting from this region, as well as the slightly high proportion of counselors/Masters-level professionals. Second, the average hourly wage for women was over two dollars higher than that for men ($17.50 versus $15.46). We attribute this to the relationship between gender and position type. While men made up roughly one quarter of the staff responding to the survey overall, nearly two-fifths of the staff in the lowest-paid position category (aids/techs) were male, and only 15% of the staff in the highest-paid of the well-populated position categories (RNs) were male. 53 variable that did so. The mean hourly wage and lowest and highest wage ranges for staff in each of the four service type categories are shown in Exhibit 5.8. Mental health staff had the highest mean hourly wage at $17.41 per hour, over six dollars per hour higher than the staff in the lowest-paid service type, developmental disabilities and mental health or substance abuse care. In the regression the significance of service type resulted from the difference between mental health staff (the reference category) and staff in programs serving people with co-occurring developmental disabilities and behavioral health needs as well as the difference between mental health staff and substance abuse staff. The pay difference between mental health staff and staff in programs providing both mental health and substance abuse services was not significant. Exhibit 5.8: Pay by Program Service Type Mean Hourly Wage Lowest Wage Reported Highest Wage Reported Mental Health (N=372) $17.41 Less than $10.00 $50.00 or more Substance Abuse (N=70) $15.10 Less than $10.00 $25.00 - $29.99 Co-occurring Mental Health & Substance Abuse (N=303) $16.96 Less than $10.00 $50.00 or more Co-occurring Developmental Disabilities & Mental Health or Substance Abuse (N=57) $11.23 Less than $10.00 $25.00 - $29.99 Overall (N=802) $16.60 Less than $10.00 $50.00 or more Mean wages based on midpoint of pay range selected ♦ Data from the staff and program manager surveys. Mean hourly wages and pay ranges for staff in each of the three consumer population
Object Description
Description
Title | Oklahoma Behavioral Healthcare Workforce Study Final Report 6 21 11 |
OkDocs Class# | M1400.3 S797b/s 2011 |
Digital Format | PDF, Adobe Reader required |
ODL electronic copy | Downloaded from agency website: Oklahoma%20Behavioral%20Healthcare%20Workforce%20Study%20Fina... |
Rights and Permissions | This Oklahoma state government publication is provided for educational purposes under U.S. copyright law. Other usage requires permission of copyright holders. |
Language | English |
Full text | 1 OKLAHOMA BEHAVIORAL HEALTHCARE WORKFORCE STUDY: DRAFT STATEWIDE REPORT · Separation Rates & Staff Intention to Leave · Vacancy Rates · Organizational Benefits & Staff Pay Rates · Staff Work Experience & Job Satisfaction Oklahoma Behavioral Healthcare Workforce Study: Final Statewide Report June 21, 2011 This research was supported by the Mental Health Transformation State Incentive Grant, 5U79SM057411 from the U.S Department of Health and Human Services, Substance Abuse and Mental Health Services Administration. The views expressed do not necessarily reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices or organizations imply endorsement by the U.S. Government. Principal Contributors include: John Hornik, Ph.D., Jenneth Carpenter, Ph.D., and Jeanine Hanna, Ph.D., Advocates for Human Potential, Albany, NY and David Wright, Ph.D. and Lorrie Byrum, M.A., Oklahoma Dept. of Mental Health and Substance Abuse Services, Oklahoma City, OK. Please see page 7 of the report for acknowledgement of the many other contributors to this study. 2 Oklahoma Behavioral Healthcare Workforce Study Statewide Report TABLE OF CONTENTS Executive Summary 3 Acknowledgements 7 Chapter 1: Introduction 9 Chapter 2: Staff Separations 16 Chapter 3: Vacancies and Staff Recruitment Barriers 27 Chapter 4: Current and Future Staffing Needs 33 Chapter 5: Benefits and Compensation 47 Chapter 6: Staff Work Experience and Job Satisfaction 56 Chapter 7: Workforce Capacity 62 Chapter 8: Representation of Consumers and Their Family Members in the Workforce 72 Chapter 9: Discussion and Recommendations 80 References 84 Appendix A: Staff Separations 85 Appendix B: Vacancies and Recruitment Barriers 98 Appendix C: Benefits and Compensation 100 Appendix D: Work Experience and Job Satisfaction 102 Appendix E: Workforce Capacity 107 3 EXECUTIVE SUMMARY The Oklahoma Behavioral Healthcare Workforce Survey and associated studies were conducted by Advocates for Human Potential, Inc. (AHP) through a contract with the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS) to assist with evaluation activities related to Oklahoma’s behavioral health transformation initiative. The statewide survey included three components: · an organizational survey focused on organizational accreditation, benefits and basic information on organizational structure; · a program manager survey related to program staffing, vacancy, recruitment barriers, causes of staff turnover, program and staff capacity and training needs; and · a staff survey focused on work experience, job satisfaction, education, training, and demographic characteristics (including status as current or prior consumers or family members of consumers). The data collection process was structured so that the three components could be linked. Participating organizations were recruited in industry groups, generally according to state agency funding and oversight. While the workforce survey was the largest component of this project and is the primary focus of this report, additional data sources were used. These include: Economic Modeling Systems, Inc. (EMSI) data provided by the Oklahoma Department of Commerce; Oklahoma data drawn from a University of North Carolina (UNC) staffing needs study; and information on historical and anticipated behavioral healthcare-related degree completion rates from the Oklahoma State Regents of Higher Education. The results of the survey and the analysis of additional data sources were reviewed with stakeholders, including external key informants, informants involved with the project, and a Workforce Study Team convened as an advisory group to the study. Based on these reviews, the following key findings were identified: · Inadequate salaries are widespread and are believed to have significant implications for both recruitment and retention. Over half of all staff members responding to the survey reported earning less than $15.00 per hour, with close to one-fifth earning less than $10.00 per hour. Assuming a 40-hour workweek, staff in this latter pay group only fall above the 2009/2010 poverty line if they have no dependents. Those with one or more dependents are living in poverty, despite being employed full-time in a challenging and critical industry. Not surprisingly, less than half of responding staff indicated that they were satisfied or very satisfied with their pay. Over half of all responding program managers also identified insufficient salary as one of the top barriers to recruiting qualified staff for their programs, and nearly two-thirds identified dissatisfaction with salary as one of the top causes of staff turnover in their programs. Indeed, pay was both the most frequently cited barrier to recruitment and most frequently cited cause of turnover. · Staff separation rates are high and relate to the composition of the workforce. The median annual program separation rate was 25%, meaning that, in roughly half of the 4 participating programs, there was more than one staff departure within the past year for every four FTEs. Separation rates do not vary randomly, but rather are associated with program staffing patterns. While staff were given six position categories from which to describe their jobs, nearly all chose either counselor/therapist/social worker or aide/tech/other paraprofessional. On average, counselors made up 50% of program staff, while techs made up 39%. Program managers reported separation rates of 42% and 25% for techs and counselors, respectively. This finding is consistent with the literature indicating that higher staff experience, job level, and pay are associated with lower turnover. · There are both current and projected shortages of professional and nonprofessional staff with an insufficient pipeline of new entrants from higher education to meet the shortages. There is a substantial gap in the need for psychiatrists and other prescribers. We estimate a need for 697 prescribers and only 287 professionals (psychiatrists and advanced practice psychiatric nurses) available to meet the need, a difference of 410. While the unmet needs for other categories of behavioral healthcare providers are not as large proportionately, there are gaps in these position types as well. The rates at which institutions of higher education in Oklahoma are producing new graduates with appropriate training are not sufficient to meet these needs; in addition, the fact that salaries for both professional and nonprofessional positions in Oklahoma are consistently lower than the surrounding states and the nation as a whole is a significant barrier to attracting new individuals into service or training. · A substantial proportion of responding staff and program managers self-identify as behavioral healthcare consumers or as family members of consumers. Both program managers and staff were asked a series of questions about their status as consumers, defined as someone who is currently or has received mental health, substance abuse, and/or other addictive disorder services, or as a family member of a consumer. Nearly one-third of respondents identified as family members and over one-fifth identified as adult consumers. Consumer and family member representation was generally higher among program managers than direct care staff, and a greater proportion of respondents identified as adult consumers than as (former) youth consumers. Additionally, among staff and program managers who identified as either consumers or family members, rates of disclosure in the workplace were high. For both statuses, roughly 80% of responding program managers reported disclosing on the job, while roughly 66% of staff reported having disclosed. · Staff are relatively well-prepared to offer Cognitive Behavioral Therapy (CBT) and are less prepared to offer other Evidence-Based Practices (EBPs). Nearly three-quarters of respondents who supervise programs serving adults indicated that new, professional-level hires in their programs were well-prepared to provide CBT. The same percentage of respondents who supervise programs serving children reported that their new, professional-level hires were prepared to offer services using CBT for trauma, while over two-thirds reported that their new, professional-level hires could provide CBT for anxiety and depression. In both child and adult-serving programs, fewer program managers reported staff competence in providing other types of EBPs: For example, just over one-third of program managers supervising adult programs reported staff competence in medication management, 5 and only a little more than half of those supervising programs for children reported staff competence in interpersonal therapy (IPT). · Knowledge of psychiatric medication and its side-effects is the most common unmet training need. Program managers were asked to identify areas of unmet training need or areas in which their staff needed training and for which training was not readily available. The most frequently cited unmet training need was for knowledge of psychiatric medication and its side-effects, with one-quarter of responding program managers citing this as an unmet need. Nearly as many program managers (23%) indicated that their staff had unmet needs for communication skills training, while the third most frequently cited unmet need (17%) was for training in educating consumers’ family members about mental health and substance abuse issues. All other competencies were cited as unmet training needs by fewer than 15% of responding program managers. · Staff report high job satisfaction and a positive overall work experience. Nearly all (95%) staff respondents agreed or strongly agreed with the statement I like the kind of work that I do and 84% of staff respondents indicated that they were satisfied or very satisfied with their job overall. Many of the more specific indicators of job satisfaction and work experience were also endorsed by the majority of staff. In particular, over three-quarters of responding staff indicated that their work gives them a feeling of personal accomplishment and that they would recommend their organization as a good place to work. Similarly, over two-thirds of those responding indicated that they were satisfied with their organization, their work schedule, and the location and physical conditions of their workplace. Lower rates of satisfaction were found with pay (described earlier), opportunity for advancement, and workplace stress level. Nearly three-quarters of respondents reported being satisfied with vacation and sick leave, with state employees generally reporting higher satisfaction with benefits than those employed by private organizations. While the generalizability of these findings is limited somewhat by the staff survey response rate and the potential for selection bias (i.e., the possibility that staff who responded were more satisfied with their work), the overwhelmingly positive response to these items is worth noting. The report concludes with a review of the Workforce Study Team’s recommendations for next steps. Throughout these recommendations, the Workforce Study Team identified the need to distinguish between strategies to maintain the behavioral healthcare workforce in its current state, and those to facilitate the development of a workforce that would be fully responsive to the behavioral healthcare needs of Oklahoma’s citizens. Regarding compensation, the Team advised that current pay rates are inadequate and suggested preparing a legislative request to bring behavioral health provider pay to the regional average by 2014. The Team also suggested increasing opportunities for advancement within behavioral health organizations to alleviate recruitment and retention problems within the field, as well as providing incentives for students to receive a portion of their clinical training in state-funded service systems. Several training-related recommendations were made to increase the number of prescribers in the state and support the development of basic behavioral healthcare skills among primary medical care providers. Implementing best practices was cited as a way to respond to the study’s findings regarding staff paperwork burden as related to job satisfaction and causes of turnover. Specific recommendations regarding best practices included: expanding access to the most up-to-date 6 information on evidence-based practices; technical assistance for professionals providing mental health services and substance abuse services in state agencies; and limiting the quantity of mandatory paperwork and reporting. Finally, the Team recommended that future planning efforts include creating a Mental Health and Substance Abuse Workforce Advisory Council to help Oklahoma develop models for providing behavioral healthcare services for its citizens in the future and meeting the prospective workforce needs for Oklahoma’s future. 7 ACKNOWLEDGEMENTS This study would not have been possible without the collaboration of many different individuals and organizations, primarily within Oklahoma. In this page, we wish to acknowledge their important contributions: Karen Frensley, Director of the Oklahoma Transformation Project, provided valuable guidance and direction, as well as political support, throughout this study. From the inception of this study to its completion, we have been guided by the Workforce Study Team, a group of dedicated volunteers who designed the goals of the study, reviewed and commented on methods and findings, and contributed recommendations to this report. The Team was chaired by Nola Harrison of St. Anthony Hospital, who worked closely with us to set agendas for each meeting and to keep us all on task, as well as providing information and advice from her own experience. Other members of the group included Carolyn Archer, David Asetoyer, Sara Barry, Contessa Bass, Donna Woods Bauer, Margaret Bradford, Nichole Burland, Renea Butler-King, Dawn Carson, Sidna Chambers, Jack Chapman, Rita Cooksey, Marva Crawford-Williamson, Richard DeSirey, Hugh Doherty, Jim Durbin, Fred Eilrich, Terrie Fritz, Annette Fulton, Jim Giffin, Chuck Gressler, Amber Guerrero, Marvin Hill, Martha Holmes, Jim Igo, Lydia Johnson, Connie Lake, Tracy Leeper, Alesha Lilly, Randy McCrary, Cathy Olberding, Glenda Owen, Rebecca Pruitt, Sandy Pruitt, Jolene Ring, Cheryl St. Clair, Susie Seymour, Bill Slater, Terry Smith, Debbie Spaeth, Jeff Talent, Ross Tripp, Ashland Viscosi, Richard Wansley, and James Wineinger, We received very generous support from Aldwyn Sappleton of the Oklahoma Department of Commerce and Randy McCrary of the Oklahoma State Regents of Higher Education. They provided key current data, as well as future projections, on the state behavioral health workforce and annual degrees awarded from Oklahoma institutions of higher education respectively. Two organizations volunteered to participate in a pilot study of the three workforce surveys. We are grateful to Nola Harrison and her colleagues at St. Anthony Hospital and Terry Smith and his colleagues at Sequoyah Enterprises, Inc. for their efforts to pre-test the organizational, program, and staff surveys. Robert Powitzky of the Department of Corrections and Alesha Lilly of the Department of Health assisted in obtaining individual data from state employees who provide mental health services under the auspices of their agencies. The directors or commissioners of six Oklahoma state agencies assisted in obtaining the participation of their behavioral healthcare contract providers. The participating directors were: Gene Christian (Office of Juvenile Affairs), Terry Cline (Department of Health), Michael Fogarty (Health Care Authority), Howard Hendrick (Department of Human Services), Justin Jones (Department of 8 Corrections), and Terri White (Department of Mental Health and Substance Abuse Services). Additionally, in her role as the President of the Oklahoma Psychiatric Hospital Association, Nola Harrison, provided assistance in obtaining the participation of her member organizations. Our colleagues Alan Ellis, Joseph Morrissey, and Kathleen Thomas of the University of North Carolina provided estimates of staffing shortages in Oklahoma among psychiatrists and other prescribers of psychiatric medications. Our AHP colleagues Denise Lang, Nick Huntington, and Darby Penney, provided assistance in data collection, database management and analysis, and report editing, respectively, and our ODMHSAS colleague Steve Davis provided comments on our approach to estimating future staffing needs. Kevin Huckshorn (formerly the Director of the NASMHPD Technical Assistance Center) and Jean Carpenter-Williams of the University of Oklahoma provided consultation on workforce competencies of the adult and children’s mental health workforce, respectively. Sheryl McLain, formerly the Executive Director of the Oklahoma Health Care Workforce Center, provided guidance on the development of the workforce survey. Deborah Dennis (Policy Research Associates) and Deb Kupfer (Western Interstate Commission for Higher Education) offered insightful comments and suggestions on an earlier draft of this report. We also thank the many, many individuals working at Oklahoma behavioral healthcare provider organizations who participated in the surveys that provided key data for this report. None of the persons cited above are responsible for any errors we may have made in this report or earlier reports of this study. We are very appreciative for all of the assistance that we received over the course of this study, and we apologize if we inadvertently excluded the names of additional contributors. 9 CHAPTER 1: INTRODUCTION In 2005, Oklahoma was one of seven states (now nine) to receive a five-year Mental Health Transformation State Incentive Grant (TSIG) from the federal Center for Mental Health Services (CMHS). The purpose of this grant was to help transform state mental health systems from “broken and fragmented” systems to systems that deliver excellent mental healthcare with a focus on recovery (President’s New Freedom Commission on Mental Health, 2003). A major challenge faced by all states was assuring a stable, competent workforce available to provide needed services. The Oklahoma Behavioral Healthcare Workforce Survey and associated studies were conducted by Advocates for Human Potential, Inc. (AHP) through a contract with the Oklahoma Department of Mental Health and Substance Abuse Services (ODMHSAS) to assist with evaluation activities related to Oklahoma’s behavioral health transformation initiative. The studies were developed and implemented under the guidance of an advisory group, the Workforce Study Team, which was convened through the Governor’s Transformation Advisory Board (GTAB) Workforce Committee, as part of the Transformation initiative. Purpose and Goals State mental health authorities typically do not have empirical information about the characteristics of their current workforce. In order to fill this information gap, we undertook a number of studies, as well as searched for relevant research, that provided useful information for understanding the difficulties faced by staff providing mental health services in Oklahoma. Taken together, the workforce studies were designed with three broad goals in mind: 1. Respond to interests of GTAB Workforce Committee convened through Oklahoma’s behavioral health transformation initiative. 2. Develop behavioral health complement to information gathered through Oklahoma Healthcare Workforce Center and Oklahoma Hospital Association surveys. 3. Provide information that can be used for provider organization and state agency-level planning and advocacy. The largest of these studies was the Oklahoma Behavioral Healthcare Workforce Survey, a statewide survey that focused on staffing of agencies and programs that provide behavioral healthcare. The survey itself was intended to address six particular goals of the Workforce Study Team and other project stakeholders, including: 1. Estimate rates of recruitment, retention and turnover by position. 2. Determine reasons for leaving, including those related to wages and benefits (e.g., health insurance, schedule/shift, child care). 3. Analyze current representation of adult peers and family members in the workforce. 4. Describe linguistic (and cultural) competency of the workforce. 10 5. Describe capacity of state workforce to address current needs of clients and employers. 6. Describe current access to behavioral healthcare services in primary care settings and identify (types of) professionals delivering such services. Methodology Survey Measures Where possible, survey items were drawn from established measures. The two primary sources of items and item structure were: · Addiction Technology Transfer Center Workforce Survey: A staff and director survey instrument was developed for the Northwest Addiction Technology Transfer Center (see Addiction Technology Transfer Center Network, n.d.) and subsequently adapted for use in at least six other states. Oklahoma workforce survey items that were drawn from or based on this instrument included those relating to recruitment barriers and causes of turnover, organizational strategies for supporting staff development, and distribution of daily responsibility, as well as a number of basic demographic related items. · Federal Human Capital Survey (FHCS): The FHCS is an instrument developed by the U.S. Office of Personnel Management and used to measure employees’ job satisfaction and their perceptions of the degree to which their organization exhibits characteristics consistent with those of successful organizations. The instrument was used to survey federal employees in 2004, 2006, and 2008, with over 200,000 responses received in the 2008 use alone (United States Office of Personnel Management, n.d.). Oklahoma workforce survey items that were drawn from the FHCS include those related to staff work experience and job satisfaction. Additional items were developed and selected for the Oklahoma Behavioral Healthcare Workforce Survey with the guidance of the Workforce Study Team and outside consultation when necessary. Pilot Study The pilot study involved two organizations: a residential care provider which operates congregate care facilities in locations throughout Oklahoma, and an inpatient care provider which operates a variety of behavioral healthcare programs in the Oklahoma City area. Between the two organizations, a total of 28 distinct programs participated in the pilot. These programs provided an array of services designed to respond to a variety of consumer needs and interests. Programs ranged from long-term residential care to acute detoxification, and served children, youth, adults and older adults, and supported people with needs related to mental health, substance abuse and co-occurring disorders. The pilot study took place in June and July, 2008. In August 2008, the preliminary results of the pilot were reviewed with the Workforce Study Team, as was a report of the survey process, including challenges encountered and suggestions offered by pilot participants. Based on these reports and the discussion with the Workforce Study Team, some redundant items were eliminated, the schedule and scope of organizational recruitment was scaled back, and the recruitment material packet was revised. Data from the pilot were included in the larger data analysis of the Oklahoma Behavioral Healthcare Workforce Study. 11 Survey Structure In order to capture the range of information desired by the Workforce Study Team and other project stakeholders, the survey was designed with three components: 1. An organizational survey focusing primarily on organizational accreditation and benefits as well as basic information on organizational structure. Organizational structure information was used to create organization-specific versions of the program manager and staff surveys described below. The organizational survey component was completed by a single member of each participating organization (typically a human resources administrator in larger organizations, or the director in smaller organizations). 2. A program manager survey containing items related to program staffing, vacancy, recruitment barriers, causes of staff turnover, program and staff capacity and training needs. Within each organization, each program manager with unique supervisory responsibilities for one or more behavioral healthcare programs was invited to complete a program manager survey. Occasionally, organizations would indicate that two or more program managers supervised a single program. In these cases, AHP worked with the organization to develop a survey plan to avoid duplication of program manager responses. 3. A staff survey focusing on staff work experience, job satisfaction, education and training as well as demographic characteristics and status as current or prior consumers or family members of consumers. All direct providers of behavioral healthcare services in participating organizations were invited to complete a staff survey. As described in the recruitment subsection below, however, staff recruitment was highly dependent on program manager assistance. Data collection and process were structured so that the three components could be linked. Staff responses could be grouped by program and organization, and linked to the appropriate program data (provided via the program manager survey) and organizational data (provided via the organizational survey). 12 Exhibit 1.1: Survey Structure ORGANIZATIONAL SURVEY PROGRAM MANAGER SURVEY PROGRAM MANAGER SURVEY PROGRAM MANAGER SURVEY STAFF SURVEY STAFF SURVEY STAFF SURVEY Recruitment & Participation Organizations were recruited in industry groups, generally according to state agency funding and oversight. The following nine industry groups were recruited: · Mental Health: Organizations providing primarily mental health services and operated under contract with or by ODMHSAS. · Oklahoma Psychiatric Hospital Association (OPHA): Psychiatric hospitals or hospitals with psychiatric units within OPHA membership. · Oklahoma Department of Human Services (DHS): Organizations providing a range of residential and outpatient services for children, youth and adults with a variety of service needs and operated by or under contract with DHS. · Oklahoma Office of Juvenile Affairs (OJA): Organizations operated by or under contract with OJA, providing services to children and youth in a range of settings. · Substance Abuse: Organizations providing primarily substance abuse services and operated under contract with or by ODMHSAS. · Oklahoma Department of Corrections (DOC): Providers employed by DOC and offering mental health services within correctional facilities across Oklahoma (substance abuse services are contracted out and were therefore not included in the survey). · Other Medicaid: A random sample of organizations that were not included in any of the above groups but that do provide behavioral healthcare services and bill Medicaid. 13 · Federally Qualified Health Centers (FQHC): Organizations that provide behavioral healthcare services and have obtained the FQHC designation. · Child Guidance: Child Guidance clinics operated by the Oklahoma State Department of Health (OSDH). The number of organizations, program managers, and staff members recruited by industry group are shown in Exhibit 1.2 on the next page. When considering the implications of the results described in this section, it may be helpful to bear in mind the degree to which the responses we received can be considered representative of the views of Oklahoma behavioral healthcare agencies, program managers, and staff. Exhibit 1.2 indicates that 63% of invited organizations responded, with participation rates by industry group ranging from 41% to 100%. We can be relatively confident that responses from agencies in high participation industry groups are representative of those industry groups, but less confident of the representativeness of responses of agencies in low participation industry groups. Similarly, among participating organizations, average program manager response rates ranged from 67% to 100%, with an overall average of 72%. Among participating programs, staff response rates ranged from 4% to 100%, with an overall average of 26%. Our confidence in program manager and staff response representativeness should also vary by industry group participation rate. Additionally, within industry groups or within the sample as a whole, we can have more confidence in the representativeness of program manager responses than we can in the representativeness of staff responses. Finally, it is important to note that, as the recruitment process was driven by state agency oversight and funding, any First Nations provider organizations that are not funded or credentialed by one or more of the above state agencies were not recruited. 14 Exhibit 1.2: Participation by Industry Group Organizations Program Managers Direct Care Staff Industry Wave Date Launched Number of Responses Response Rate Number of Responses Response Rate Number of Responses Response Rate1 Mental Health 9/30/08 27 79% 102 67% 443 21% OK Psychiatric Hospitals Association 11/04/08 12 41% 32 74% 363 26% OK Department of Human Services 1/14/09 10 83% 20 74% 150 31% OK Office of Juvenile Affairs 1/14/09 11 79% 12 86% 38 13% Substance Abuse 5/14/09 38 62% 52 74% 234 36% Department of Corrections 8/17/09 12 100% 6 100% 40 63% Other Medicaid Providers 8/19/09 11 48% 9 82% 6 4% Federally Qualified Health Centers 8/19/09 5 45% 2 67% 14 100% Child Guidance Clinics 10/26/09 12 100% 8 89% 37 73% Total: 116 63% 243 72% 1325 26% 1 Staff participation rates are based on programs for which total number of staff is known. 2 The Department of Corrections and Child Guidance Clinics are multiple service sites however due to the nature of the programs they were surveyed as one organization. 15 At the beginning of the recruitment phase for each industry, enrollment packets were mailed to the organizations that had been identified for recruitment. These packets included a cover letter from the relevant state agency administrator, describing the value of the project and encouraging the organization to participate. Following this cover letter were informational sheets from AHP about the purpose of the survey and the enrollment process. A single organizational designee completed the organizational survey component online, providing program manager names and email addresses. Organizations that did not initially respond were encouraged to do so via email, telephone, and U.S. mail reminders, which included sample reports that served as an organizational incentive. Once an organization completed the organizational component of the survey, a unique version of the program manager and staff survey was created to reflect the structure of the organization. Program managers were mailed invitational emails with recruitment letters as attachments to be distributed to staff. Regular reminders were sent to program managers, including counts of staff responses for each program, which were copied to the organizational designee and/or executive director. A variety of additional measures were employed to encourage participation at each stage of the survey. For most industries, personnel from the relevant Oklahoma state agency made additional follow-up calls. Additionally, AHP staff made in-person visits to key organizations to provide assistance in participating in the survey, or to encourage participation. Other Data Sources While the workforce survey is the largest component of this project and is generally the focus of this report, data were drawn from a variety of additional sources: · Economic Modeling Systems Inc (EMSI): The Oklahoma Department of Commerce provided average hourly wage rate norms for a range of behavioral healthcare positions at the national, regional and state level. · University of North Carolina (UNC) Staffing Needs Study: Data were drawn from a UNC study of professional staffing shortages, conducted under contract to the Health Resources and Services Administration (HRSA). · Oklahoma State Regents of Higher Education: The Regents of Higher Education provided information on the number of behavioral healthcare related degrees awarded by category and by year since 2001, as well as information on the number of degrees anticipated to be granted and anticipated to be needed. The data derived from these sources complement the data collected from the survey and provide information on subjects that could not be covered by the survey. In doing so, they allow the project to provide a more comprehensive response to the Workforce Study Team’s interests and goals. 16 CHAPTER 2: STAFF SEPARATIONS Staff separation rate (turnover) is a near-universal concern in behavioral healthcare programs. High separation rates increase program costs, reduce return on investment for staff development, and impact quality of care. Anecdotal evidence of the negative impact of turnover on provider-consumer relationships abounds. Given this, it is not surprising that study stakeholders identified staff separation as a principal area for investigation. Information was gathered on staff separations through both the program manager and staff surveys. Program managers were asked to review a list of 18 possible causes of staff turnover and to indicate which of these were most relevant to their program. Managers were also asked to report on the number of separations in their program using the study’s six primary position categories. Staff members were asked to report whether they intended to leave their position within the next 12 months. This section describes the responses to these survey items, and the relationships between these items and other program, organizational and staff characteristics. Program Manager Perceptions of Causes of Turnover Program managers were asked to identify three causes of staff turnover in their programs. The causes most frequently cited by the responding program managers are shown in Exhibit 2.1.3 Percentages for this item add up to more than 100, as three causes of turnover were selected for each program. Program managers perceive dissatisfaction with salary/pay as the greatest contributor to staff separations; 63% cited dissatisfaction with pay as a significant cause of turnover in their program. Other factors cited by at least one third of the program managers were excessive paperwork (43%), emotional burnout (36%) and excessive on-the-job stress (33%). Exhibit 2.1: Program Manager Perceptions of Causes of Turnover across Industries Data from the program manager surveys. 3Causes cited by less than 10% of program managers are not shown in Exhibit 2.1. These causes were: dissatisfaction with workplace location; dissatisfaction with relationship with supervisor; dissatisfaction with on-call responsibilities; difficulties with transportation; difficulties with child care; dissatisfaction with health insurance; dissatisfaction with time off; concern about on-the-job safety; and dissatisfaction with coworkers. 17 We examined the relationships of the perceived causes of staff turnover to seven key dimensions: industry group (Mental Health, Substance Abuse, Department of Human Services, Office of Juvenile Justice, Oklahoma Psychiatric Hospital Association, Child Guidance, Federally Qualified Health Centers, Other Medicaid and the Department of Corrections)4, region (northwest, southwest, northeast, southeast, Tulsa metro, Oklahoma City metro), service type (mental health, substance abuse, combined mental health and substance abuse, and services for people with developmental disabilities and mental health or substance abuse needs), program setting (inpatient, criminal justice, residential, or outpatient), service population (children, adults, both), organizational type (state vs. private), and organizational size (small, medium, large). The following causes of turnover were significantly different (p<.05) across at least one of the seven dimensions: (1) dissatisfaction with salary/pay (Salary), (2) dissatisfaction with career ladder, (3) excessive paperwork (Paperwork), (4) dissatisfaction with job responsibilities (Responsibilities) and (5) dissatisfaction with shift/work hours (Hours). While none of these causes of turnover varied by region or service type, there was variation across industry group, program setting, service population, organizational size, and organizational operation (state vs. private), also considered a proxy for organizational benefits. Following these findings, logistic regressions were performed to examine the relationship between the dimensions - taken together - and each of the following four causes of turnover: Salary, Responsibilities, Hours and Paperwork. Industry, service population, organizational type, program setting, and organizational size were included in this testing. Tables summarizing the results of these regressions can be found in Appendix A1. Four additional parsimonious logistic regression models can be found in Appendix A1 as well, for a total of eight regression models. In summary, when controlling for other factors, program manager perceptions of causes of staff turnover suggest that: 1. The role of salary/pay in turnover varies by industry. 2. The role of excessive paperwork and dissatisfaction with job responsibilities in turnover varies by service populations. 3. The role of excessive paperwork in turnover also varies by program settings. Pay as a Perceived Cause of Turnover Exhibit 2.2 provides details of the relationships between organizational industry and pay as a perceived cause of turnover.5 Program managers in OJA organizations were most likely to cite pay as a cause of turnover, while those in OPHA organizations were least likely to do so. Specifically, 90% of program managers from the OJA industry group perceived staff dissatisfaction with salary/pay as one of the top reasons for staff separations while program managers from the OPHA industry group were only half as likely to name dissatisfaction with salary/pay. At least 70% of program managers from the Mental Health and DHS industry groups cited salary/pay as a cause of turnover. This relationship was upheld in the regression analyses as well, with industry being a significant predictor of program manager perceptions of pay as a 4 Industry group name and abbreviation: Mental Health (MH), Substance Abuse, Department of Human Services (DHS), Office of Juvenile Justice (OJA), Oklahoma Psychiatric Hospital Association (OPHA), Child Guidance, Federally Qualified Health Centers (FQHC), Other Medicaid (MA) and the Department of Corrections (DOC). 5 Industries with fewer than ten program manager responses were not included in the analysis. 18 significant cause of turnover. Program setting, organizational size, and organizational6 operation were not significant in the logistic regression model. Exhibit 2.2: PM Perceptions of Pay as a Cause of Staff Turnover by Industry MH N=102 DHS N=17 OJA N=10 OPHA N=26 SA N=61 Dissatisfaction with salary/pay 76% 71% 90% 42% 53% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. ♦ FQHC, DOC, Other Medicaid, and Child Guidance industries are not included in the analysis because there were fewer than ten programs in these samples. Excessive Paperwork as a Perceived Cause of Turnover Exhibit 2.3 shows the relationship between program manager perception of excessive paperwork as a cause of staff turnover and program setting. Respondents characterized their program setting as one of the following: inpatient (an acute care mental health unit in a hospital, a unit in a substance abuse detoxification facility, or a residential unit within a hospital), outpatient (a unit in a community mental health center, a day program, a psychiatric rehabilitation program or a Program of Assertive Community Treatment/case management program), residential (a group home or a supported housing program), and correctional/criminal justice (a prison or juvenile detention facility). Excessive paperwork was cited as a cause of separations by 60% of program managers from outpatient facilities, 21% of those managing residential programs, 20% of those managing inpatient units, and 10% of those managing programs in criminal justice facilities. The relationship between program setting and excessive paperwork remained when the effects of other variables were considered. Industry group and excessive paperwork had a strong relationship when that relationship was tested on its own, but it did not remain significant in the regression analysis. Service population (adults, children, or both adults and children) was unrelated to paperwork as a cause of turnover when this relationship was tested alone, but became a significant predictor in the regression analysis (Model 2 of the logistic regressions). Program managers in programs serving children cite excessive paperwork as a cause of turnover more frequently than those serving both children and adults. Exhibit 2.3: PM Perceptions of Paperwork as a Cause of Staff Turnover by Program Setting Inpatient N=30 Outpatient N=119 Residential N=47 Correctional N=10 Excessive paperwork 20% 60% 21% 10% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. Dissatisfaction with Job Responsibilities as a Perceived Cause of Turnover Dissatisfaction with job responsibilities varied by service population (Exhibit 2.4), with program managers supervising programs serving both children and adults being less likely to perceive job 6 Organizational size – programs are the unit of analysis. Program managers were asked to identify the number of full-time staff working in each program they supervised. The number of full-time staff were aggregated for each organization. An organizational response rate was calculated and the total number of staff in each organization was divided by the organizational response rate and multiplied by 100. This yielded the total number of full-time staff in each organization (i.e., total staff) which was then divided into three groups – small, medium and large organizations – based on the overall distribution of the total staff. 19 responsibilities as one of the most important causes of staff turnover than were managers supervising programs that serve only adults or only children. While this relationship may not initially seem meaningful, it could be related to the relationship between service population and program setting. Eighty percent of programs serving both children and adults were categorized as outpatient programs. Compared to program managers in inpatient and residential programs, fewer outpatient program managers cited job responsibilities as a significant cause of turnover in their programs. The relationship between job responsibilities and service population is further supported by Model 3 of the logistic regressions (see Appendix A1 - Factors Influencing Program Manager Perceptions of Staff Dissatisfaction with Job Responsibilities as a Cause of Turnover). Organizational size was not significant in the regression model. Although dissatisfaction with job responsibilities varied by industry, program setting, and service population, these were not significant predictors in the full regression model. Exhibit 2.4: PM Perceptions of Responsibilities as a Cause of Staff Turnover by Service Population Children/Adults Adults Only Children Only Dissatisfaction with job responsibilities 4% 21% 25% Data are significant at the p<.05 level. ♦ Data from the program manager surveys. Program Manager-Reported Separation Rates Program managers were asked to report the current number of full time equivalents (FTEs) budgeted for their program and vacant in their program, as well as the number of staff separations that occurred over the previous 12 months in their program. These items were posed in reference to each of six position categories: aides/techs/other paraprofessionals, professionals primarily holding Masters degrees (counselors/therapists/MSW-level social workers), LPNs, psychiatrists and other physicians, doctoral-level psychologists/DSW-level social workers, and RNs7. Exhibit 2.5 shows the position-specific and total separation rates statewide, and for each of the six geographic regions8. 7 This position category structure was developed based on a review of the state position classification and the U.S. Bureau of Labor Statistics Standard Occupational Code (SOC) system. Appendix A15 shows relevant SOC positions categorized according to this six-position structure. 8 To calculate the separation rate for a given region, the number of separations was totaled across participating programs, and this sum was divided by the number of FTEs budgeted across programs. It is important to note that organizations may not have included providers that are contracted with, rather than employed, in the counts that follow. 20 Exhibit 2.5: Cross-industry Program Manager-Reported Separation Rates by Region Position NE NW OKC SE SW Tulsa Statewide Aide/tech 51% 55% 34% 38% 50% 30% 42% Masters-level professional 28% 26% 26% 27% 8% 27% 25% LPN 32% 29% 40% 50% 33% 10% 36% Psychiatrist/ physician 33% 0% 4% 44% 25% 20% 22% Psychologist 13% 0% 0% 0% 0% NA 7% RN 25% 33% 29% 56% 23% 21% 28% Total 40% 41% 31% 35% 32% 27% 34% Data from the program manager surveys. Calculating Program Separation Rate Percents in the table above were calculated by summing separations and budgeted positions across the region. In the analysis that follows, separations are calculated at the program level. Programs, rather than organizations, were chosen as the unit of analysis due to concerns that program characteristics and local program environment may vary widely within larger organizations - particularly those with programs across a wide geographic range. Program separation rates ranged from 0% to 200%. Separation rates of greater than 100% are possible because positions may turn over more than once within a year. The median separation rate was 25%, meaning that roughly half of the participating programs had a separation rate below 25%, and roughly half had a separation rate above 25%. In other words, at least one out of every four positions turned over in roughly half of the programs surveyed. Appendix A2 gives more information on the distribution of the program separation rates. The initial analysis of the relationships between separation rates and other program variables was attempted with three approaches to handling separation rates: by breaking participating programs first into two groups of equal size, then into three groups of equal size, and finally into four groups of equal size. The approaches yielded fairly similar results, with those for the two group approach being slightly more favorable than those for the alternatives. This approach involves dividing the group at the median of 25%, a rate which is consistent with a high turnover definition used in a recent, related study (Strolin-Goltzman, 2008). Relationships between Separation Rates and Other Program Variables The relationship between separation rate and a number of program characteristics and related variables was examined. Relevant, recent literature was reviewed. The following identifying program characteristics were identified as being potentially related to separation rates: 1. Staff role clarity 2. Staff job satisfaction 3. Staff salary and benefits 4. Staff sense of personal accomplishment 5. Staff age 6. Staff intention to leave 7. Staff job level/experience 21 8. Staff burnout 9. Lack of alternative job options The primary source of information for items 1- 6 is the staff survey. Because of concerns about the representativeness of the staff data, these items were not considered feasible for this analysis. Most of these variables are also established predictors of staff intention to leave, and could therefore be employed in the predictive model of intention to leave (itself the strongest predictor of separation rates, Mor Barak et al., 2001). Staff job level/experience as a program characteristic was measured using the program manager reports of the FTEs budgeted for their programs. As these reports were specific to position type, we were able to create variables reflecting the proportion of each position type within each program’s staffing pattern. Masters-level counselors and techs made up by far the largest proportion of program staff. On average, Masters-level counselors made up 50% of the program staff, and techs made up 39%, a significant finding in and of itself. The remaining four position categories ranged from a high of 6% (RNs) to a low of 1% (PhDs). Appendix A3 offers more information about the distribution of each of the six position type proportions. Staff burnout as a program characteristic was measured by program manager indication that burnout is one of the top three reasons for staff turnover within their program. We also looked for relationships between the other frequently-cited causes of turnover and separation rate. A proxy for lack of alternative job options was created using the program region code: Programs located in the Tulsa and Oklahoma City metro areas were considered to be located in areas with better alternative job options, while those in the remaining, more rural, regions were coded as being located in areas with fewer job options. Finally, relationships between separation rate and each of the study dimensions described earlier (industry, region, service type, program setting, population age, state operation, and organizational size) were examined. Analysis and Results Analysis to identify relationships between separation rate and each of the variables above on an individual basis was performed. Most of these did not prove to be statistically significant: None of the frequently-cited causes of turnover were associated with program separation rate, nor was the job options proxy. Of the staffing and study dimensions variables, proportion of Masters-level counselors, proportion of techs, industry, and state operation were significantly associated with separation rate, as were two approaches at measuring benefits9. Further information about the items and the relationships identified may be found in Appendix A4. Logistic regression was then performed to test the relationships between separation rate and multiple predictor variables. Looking at the relationship between the two staffing variables (proportion of techs and proportion of Masters-level counselors) it was determined that these variables were too closely related to include in the regression model. Details of the analysis used 9 Upon closer inspection, the results for the benefits items were difficult to interpret (i.e., suggesting an inconsistent or nonsensical relationship between separation rate and benefits). These items were discarded. 22 to determine this can be found in Appendix A5. Ultimately, the model included the following program characteristics: proportion of techs, industry10, and state operation. Both proportion of techs and state operation remained significant in the regression model. As shown in Exhibit 2.6, on average techs made up 31% of the staff in low separation programs, while they made up nearly half of the staff in high separation programs. This is consistent with the literature indicating that high staff experience, job level, and pay are associated with lower turnover. Exhibit 2.6: Proportion Techs in Low Separation and High Separation Programs Staff position type predictors Mean proportion low separation programs Mean proportion high separation programs Proportion Techs 31% 48% Data from the program manager surveys. A more detailed look at the relationship between position type and separation is available in Appendix A7. The distribution for programs in state vs. privately operated organizations is also as anticipated. Half of the programs in private organizations fall into the high turnover group, while less than one-third of the programs in state operated organizations do (Exhibit 2.7). It is believed that this relationship is at least in part a result of the better benefits package offered by state operated organizations. Exhibit 2.7: Proportion of Programs in High Separation Group by State/Private Operation Operation (assigned) Private (N=188) State (N=56) Proportion in high turnover group 50% 29% Data from the program manager surveys. While only a proportion of techs and state operation remained significant in the regression model, Appendix A8 offers details on the remaining variables that were tested. Staff Intention to Leave Staff were also asked about their plans to leave their organizations within the next year. Those who reported that they were planning on leaving were asked to indicate whether they planned to retire, find another job within the behavioral healthcare field, find a job outside the field, or pursue some other option. Exhibit 2.8 shows the percentages of program managers and staff reporting each of these plans. 10 While proportion of techs and state operation remained significant in the regression model, industry became insignificant, suggesting that the relationship between industry and separation rate may have been in part due to a relationship between industry and state operation, or possibly between industry and staffing patterns. A detailed look at the results of this model can be found in Appendix A6. 23 Exhibit 2.8: Intention to Leave Frequencies Response (N=1244) % No, don’t intend to leave within a year 80% Yes, to retire 1% Yes, to take another job in behavioral health 7% Yes, to take a job outside behavioral health 4% Yes, other 7% Data from the staff surveys. This variable was recoded into two categories by combining all categories representing any intention to leave (do intend to leave within a year: 19%, and do not intend to leave within a year: 80%) for the analysis that follows. Relationship between Intention to Leave and Staff and Program Variables As with separation rates, predictor variables were chosen following a review of the literature. This review supported the use of the following variables: 1. Staff burnout; 2. Work-life fit; 3. Job satisfaction; 4. Empowerment; 5. Workplace incivility; 6. Staff age; 7. Job level/experience; 8. Professional and job commitment; and 9. Income. Staff burnout was not measured directly by the staff survey. Related items, such as My workplace is too stressful, would have appeared to provide reasonable proxies but more closely matched other predictors examined in and not supported by the literature. The same holds true for workplace incivility and empowerment. The survey did not examine work-life fit or professional/job commitment. The survey’s overall job satisfaction item was chosen as an indicator of job satisfaction. The survey’s staff age variable was transformed into a continuous variable by recoding age categories into midpoints, except for over 64 which was recoded as 69.5, the midpoint between 65 and 74. The survey’s categorical staff income variable was treated in a similar manner, with the following differences: The lowest category (<$10.00/hr) was recoded as the midpoint between $10.00 and $7.25, the minimum wage in Oklahoma. Position types and education level for respondents who checked the upper category ($50.00/hr or more) were examined, and were surprisingly found to be primarily Masters-level therapists, along with a few physicians. For this reason, we used a rate relatively close to the second-highest category, and significantly below one that might be expected for physicians: $62.50. Staff responses to the item How many years 24 have you been in the field? were used to measure staff experience. Detailed information on the distribution of these variables is offered in Appendix A9. Gender and ethnicity were tested using the original dichotomous survey items, and race was tested by collapsing five dichotomous survey items into a single variable with up to six categories: American Indian/Alaskan Native alone, Asian alone, Black/African American alone, Native Hawaiian/Pacific Islander alone, White alone, and more than one race. Due to low Ns, the Asian alone and Native Hawaiian/Pacific Islander alone categories were eliminated from the analysis. In addition to the variables gathered through the staff survey, the relationship of staff intention to leave to key program variables was investigated, including the program manager-cited causes of turnover and the study dimensions described earlier (industry, region, service type, program setting, population age, state operation, and organizational size). Analysis and Results As with the analyses described earlier, relationships were examined between intention to leave and each of the variables described above on an individual basis. As with separation rate, there was no relationship between intention to leave and program manager citation of the significant causes of turnover. Of the study dimensions, only service type and region were significantly related to staff intention to leave. Staff position type, gender, ethnicity and race were not significant, but staff age, experience, pay and job satisfaction were significant. Initially, the relationship between consumer or family status and intention to leave was investigated by collapsing eight dichotomous survey items into a single four-category variable: neither, consumer only, family member only, and both consumer and family member. This variable was significantly related to intention to leave. However, the distribution was difficult to interpret.11 A variety of alternatives were tested, including the eight original survey items.12 Most of these tests did not yield significant results. However, family status did prove to be significantly related to intention to leave, with a higher proportion of family members than non-family members indicating that they planned to leave within the next year.13 11 Staff who identified as being consumers only seemed much less likely to intend to separate than did staff who identified as either family members or both consumers and family members (full details available in Appendix A10). Given this, it seemed possible that the use of this collapsed variable could be obscuring the meaning of the relationship. 12 Eight original survey items include: adult mental health consumer, adult substance abuse consumer, former youth mental health consumer, former youth substance abuse consumer, family member of an adult mental health consumer, family member of an adult substance abuse consumer, family member of a youth mental health consumer, family member of a youth substance abuse consumer. Aggregations of these items across two dimensions individually and together (adult/youth and mental health/substance abuse) were also tested. 13 When family membership was broken down further into mental health and substance abuse, the relationship between being a family member of a mental health consumer and intention to leave was significant, while that between being a family member of a substance abuse consumer and intention to leave was not significant. However, as the latter relationship showed a similar trend (higher intention to leave among family members), the combined mental health and substance abuse variable was retained for further analysis. 25 Logistic regression was employed to determine whether the relationships noted above remained significant when the effects of all variables were considered.14 The model tested included region, service type, population age, job satisfaction, pay, age, experience, and family member status. Of these variables, only job satisfaction and age remained significant. The mean satisfaction score for staff not intending to leave was 1.71, with 1 being very satisfied and 2 being satisfied (Exhibit 2.9). The mean for staff intending to leave was 2.59, closer to 3, or neither satisfied nor dissatisfied. Consistent with literature on the topic, staff intending to leave were younger on average than those not intending to leave (39.67 years versus 43.30 years, respectively). Complete details on the results of the regression model are shown in Appendix A12, and additional details on the relationship of job satisfaction and staff age to intention to leave are shown in Appendix A13. Exhibit 2.9: Satisfaction and Age Among Staff Intending to Stay and Intending to Leave Mean for staff staying Mean for staff leaving Staff overall job satisfaction (N=1241) 1.71 2.59 Staff age (N=1180) 43.30 39.67 Data from the staff surveys. While only these two variables remained significant in the regression model, Appendix A14 gives additional information on the other variables tested. Summary Information related to separations was gathered through program manager reports of the perceived causes of separation in their programs, program managers’ reports of their programs’ separation rate over the previous year, and staff reports of their intention to leave their position within the next year. The most frequently cited barrier was dissatisfaction with pay, which was cited by nearly two thirds of program managers. Excessive paperwork, emotional burnout and excessive on-the-job stress were cited by at least one third of program managers. While program and organization characteristics were related to multiple perceived causes of turnover when the relationships were examined individually, generally only one or two characteristics remained significant in each logistic regression model. Organizational industry was a significant predictor of citing dissatisfaction with pay, with OPHA program managers being the least likely to cite pay as a cause of turnover. Population age and program setting were significant predictors of perceiving paperwork to be a cause of turnover, with program managers in programs serving children citing paperwork more frequently than those serving both children and adults, and program managers in outpatient settings citing paperwork more frequently than program managers in other settings. Service population also related to citation of dissatisfaction with job responsibilities, with program managers from programs serving both children and adults being less likely to cite this as a barrier than program managers from programs serving either adults or children. 14 We began by examining the relationship between staff age, experience, pay, and job satisfaction. While there were some relationships among these variables, none turned out to be strong enough to warrant excluding any of the variables from the regression model. Details of this analysis can be found in Appendix A11. 26 Program separation rates ranged from 0% to 200%, with roughly half of the participating programs having a separation rate below 25% and roughly half having a separation rate above 25%. This median rate was used to divide programs into two categories: low separation and high separation. These categories were related to multiple program and organizational characteristics when the relationships were examined individually, but only two characteristics remained significant in the logistic regression model. High separation programs proved to be less likely to be state operated, and more likely to have a high proportion of techs on staff. On average, techs made up less than one third of the staff in low separation programs, but nearly one half of the staff in high separation programs. These results are consistent with existing literature regarding the relationship between lower staff experience/job level and higher separation rates. The vast majority (80%) of staff did not report intending to leave their positions within the 12 month period following the survey. Intention to leave was related to a range of program, organizational and staff characteristics when the relationships were examined individually, but only two remained significant in the logistic regression model. As would be expected, staff intending to leave reported lower satisfaction with their job overall. Staff age was also related to intention to leave, with the mean age for staff intending to leave being about three and a half years younger than that of staff intending to stay. Both of these findings are consistent with the literature on staff intention to leave. 27 CHAPTER 3: VACANCIES AND STAFF RECRUITMENT BARRIERS Like staff separations, position vacancies are an area of concern in many behavioral healthcare programs. We collected information on position vacancies on two issues: First, program managers were asked to review a list of 19 possible barriers to staff recruitment, and to indicate which of these were most relevant to their program. Second, program managers were asked to report on the current vacancies in their program, using the six position categories described earlier. This section describes the program managers’ responses, and the relationships between these variables and program characteristics. Program Manager Perceptions of Recruitment Barriers Program managers were asked to identify the top barriers to filling staff vacancies in their programs. The barriers cited most frequently are shown in Exhibit 3.1.15 As each program manager was asked to identify three barriers, the percentages for this item add up to more than 100. The most frequently cited barrier was salary/pay, with 57% of program managers identifying this as an obstacle to filling vacancies in their programs. Lack of candidates with desired credentials or work experience, small applicant pool due to geographic location, and competition from other fields were cited as barriers by at least 25% of program managers. Exhibit 3.1: Program Manager Perceptions of Recruitment Barriers Data from the program manager survey. 15 Barriers cited by less than 10% of program managers are not shown in Exhibit 3.1. These barriers are: cumbersome hiring process; career ladder not attractive; childcare not offered; organizational facilities not attractive; organizational reputation; negative stereotypes of service consumers; job responsibilities not attractive; amount of training required; cost of training required; and benefits not attractive. 28 Recruitment Barriers and Program Variables The next four tables illustrate how recruitment barriers varied by industry, region, organizational size and type.16 The following six barriers to recruitment were used in the analysis that follows: 1. Salary/pay not attractive; 2. No candidates with desired credentials; 3. No candidates with desired work experience; 4. Competition from other fields; 5. Problems with funding/not allowed to fill a position; and 6. Shift/work hours not attractive. Industry and Recruitment Barriers All six of the perceived barriers varied by industry. Eighty percent of program managers in the OJA industry group identified salary/pay as one of the most critical barriers to filling vacancies, while only 19% of OPHA industry group program managers cited this as a recruitment barrier. No OJA program managers cited difficulty finding candidates with desired credentials, but roughly two fifths of Mental Health and Substance Abuse industry program managers perceive this to be a recruitment barrier in their programs. Competition from other fields also varied by industry. Program managers working in the OJA industry group were more likely to cite this as a barrier to staff recruitment (70%) than program managers from any other industry group. One third of program managers from the Substance Abuse industry group perceived funding or not being allowed to fill a position to be one of the most pertinent causes of vacancies; only 10% to 15% of program managers from other industries cited this as a barrier to recruitment. Not surprisingly, shift/work hours is more frequently perceived as a barrier by program managers in industries with a high proportion of 24-hour programs (OPHA, OJA). Finally, while nearly one third of OJA program managers perceive the hiring process itself to be a barrier, this process was not cited as a barrier by any Substance Abuse industry program managers. Exhibit 3.2: Program Manager Perceptions of Recruitment Barriers by Industry Perceived Barrier MH N=101 DHS N=17 OJA N=10 OPHA N=26 SA N=61 Salary/pay not attractive 74% 59% 80% 19% 49% No candidates w desired credentials 37% 24% 0% 15% 41% Competition from other fields 32% 12% 70% 31% 15% Funding/not allowed to fill position 14% 12% 10% 15% 33% Shift/work hours not attractive 17% 24% 40% 42% 15% Cumbersome hiring process 11% 18% 30% 15% 0% Data from the program manager surveys. ♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. ♦ FQHC, DOC, Other Medicaid, and Child Guidance industries are not included in the analysis due to the low number of programs responding to these items. 16 Barriers to recruitment did not vary by service type; as a result, service type was not included in the analysis. 29 State Operation and Recruitment Barriers Organizational operation (state vs. private) was related to three barriers to recruitment. As shown in Exhibit 3.3, nearly three-quarters of program managers from state operated organizations cited salary as a barrier, in comparison to just over half of program managers from privately operated organizations. OPHA organizations may be playing a role in this finding: OPHA program managers were significantly less likely to cite salary as a barrier, and OPHA is the only industry group composed entirely of private organizations. Program managers from state operated organizations were also significantly less likely than those from private organizations to cite lack of candidates with desired work experience as a recruitment barrier. Finally, state operated organizations were more likely than privately operated to cite funding as a fundamental obstacle to staff recruitment. Exhibit 3.3: Program Manager Perceptions of Recruitment Barriers by Organizational Type Perceived Barrier State N=53 Private N=181 Salary/pay not attractive 74% 52% No candidates with desired experience 6% 34% Funding/not allowed to fill position 42% 17% Data from the program manager surveys. ♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Organizational Size and Recruitment Barriers Organizational size was associated with program manager perception that salary and lack of staff with desired credentials are recruitment barriers. Program managers affiliated with large organizations (those with an estimated staff size of at least 82 full time employees) cited salary/pay as a reason for staff vacancies more often than those affiliated with small or medium organizations. Further analysis indicated that small organizations (those with an estimated staff size of less than 15 full-time employees) had more professional staff – staff in positions requiring additional education – and were less likely to be inpatient facilities requiring a large number of aides/techs, who typically earn the lowest salary among direct care staff. These differences in staffing patterns may also relate to the finding that program managers in small organizations are the most likely to cite lack of candidates with desired credentials as a barrier to recruitment. Exhibit 3.4: Program Manager Perceptions of Recruitment Barriers by Organizational Size Perceived Barrier Small Orgs N=33 Medium Orgs N=53 Large Orgs N=126 Salary/pay not attractive 42% 45% 67% No candidates with desired credentials 52% 30% 27% Data from the program manager and organizational surveys.♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Region and Recruitment Barriers Finally, geographic region was significantly related to four of the perceived recruitment barriers: absence of candidates with desired work experience, small applicant pool due to geographic location, competition from other fields, and location of agency not attractive. Not surprisingly, 30 two of these barriers are explicitly location-based, and a third (lack of candidates with desired work experience) could also be argued to be intrinsically tied to location or area. As shown in Exhibit 3.5, a small pool of applicants is the greatest barrier to filling vacancies in the northeast and southeast quadrants of the state, while about half of the program managers from the northwest indicated that competition from other fields was a problem with respect to vacancies in the behavioral healthcare field. Exhibit 3.5: Program Manager Perceptions of Recruitment Barriers by Region Perceived Barrier NE N=54 NW N=14 OKC N=74 SE N=32 SW N=30 Tulsa N=26 Statewide N=230 No candidates w desired work experience 15% 21% 30% 19% 40% 46% 29% Small applicant pool due to geographic location 52% 43% 7% 47% 23% 4% 29% Competition from other fields 19% 50% 34% 28% 7% 42% 30% Location of agency not attractive 35% 14% 8% 13% 3% 0% 12% Data from the program manager surveys.♦ Items cited by fewer than 10% of program managers are not included in the exhibit. ♦ All data are significant at the p<.05 level. Salary as a Perceived Recruitment Barrier Given that salary was the most frequently cited recruitment barrier as well as the most frequently cited cause of separations, it warranted further exploration. Logistic regression was used to test the three program variables discussed above (industry, state operation, and organization size) as predictors of salary as a barrier to recruitment. While organization size did not remain significant, both industry and state operation were significant: OPHA program managers were significantly less like than Mental Health industry managers to cite salary as a barrier, and program managers in state operated organizations were significantly more likely to cite salary as a barrier than were those in privately operated organizations. As noted earlier, the significant relationship between salary as a perceived barrier and industry may be attributable to the low proportion of OPHA program managers citing salary as a barrier. Further details on the results of this regression model may be found in Appendix B1. Program Manager-Reported Vacancy Rates As reported in the section on separations, program managers were asked to report the current number of full time equivalents (FTEs) budgeted for their program and vacant in their program. These items were posed in reference to each of six position categories presented in Exhibit 3.6. To calculate the vacancy rate for a given region, the number of vacancies was totaled across participating programs, and this sum was divided by the number of FTEs budgeted across programs. Exhibit 3.6 shows the position-specific and total vacancy rates statewide, and for each of the six geographic regions. It is important to note that organizations may not have included staff that they contract with (rather than employ) in the counts that follow. 31 Exhibit 3.6: Cross-Industry Vacancies by Region Position NE NW OKC SE SW Tulsa Statewide Aide/tech 7% 13% 8% 7% 16% 8% 9% Masters-level professional 15% 9% 12% 11% 2% 36% 15% LPN 4% 14% 9% 20% 33% 0% 9% Psychiatrist/ physician 3% 0% 0% 33% 13% 10% 7% Psychologist 6% 50% 0% 0% 0% NA 7% RN 13% 22% 15% 28% 8% 7% 14% Total 10% 13% 10% 12% 10% 17% 11% Data from the program manager surveys. Calculating Program Vacancy Rate Percents in the table above were calculated by summing vacancies and budgeted positions across the region. In the analysis that follows, vacancies are calculated at the program level.17 Program vacancy rates ranged from 0% to 100%. The median vacancy rate was 4%, meaning that roughly half of the participating programs had a vacancy rate below 4%, and roughly half had a vacancy rate above 4%. Appendix B2 gives more information on the distribution of the program vacancy rates. Relationships between Vacancy Rates and Other Program Variables We examined the relationship between vacancy rate and a number of program characteristics and related variables. Programs were categorized as either having a low vacancy rate (less than 5%) or high vacancy rate (5% or higher). We began by testing for relationships between vacancy rate and each of the frequently-cited recruitment barriers. Then, as with separation rate, we looked for a relationship between staffing patterns (e.g., proportion Masters-level counselors, proportion techs) and vacancy rate. Finally, we looked for relationships between vacancy rate and each of the study dimensions described earlier.18 Analysis and Results We began by performing analysis to identify relationships between vacancy rate and each of the variables above on an individual basis. Only one of the identified variables proved to be related to vacancy rates:19 High vacancy programs had a greater proportion of RNs than low vacancy programs. As shown in Exhibit 3.7 the average proportion RNs for low vacancy programs was 4%, while the average for high vacancy programs was 7%. While this difference may appear relatively small, it was statistically significant. This finding may be related to the comparatively high rate of vacancies among RN positions overall. As noted earlier in Exhibit 3.6 the overall vacancy rate for RN positions was comparable to that for Masters-level counselors, which was 17 As noted in the separation section, programs were chosen as the unit of analysis due to concerns that program characteristics and local program environment may vary widely within larger organizations - particularly those with programs across a wide geographic range. 18 Industry, region, service type, program setting, population age, state operation, and organizational size. 19 None of the frequently cited recruitment barriers were associated with program vacancy rate, nor were any of the study dimension variables. 32 the position type with the highest vacancy rate. Additional information on the (non-significant) findings for the remaining variables may be found in Appendix B3. Exhibit 3.7: Proportion RNs in Low Vacancy and High Vacancy Programs Staff position type predictors Mean proportion low vacancy programs Mean proportion high vacancy programs Proportion RNs 4% 7% Data from the program manager surveys. Summary Information related to vacancies was gathered through program managers’ reports of the perceived recruitment barriers in their programs, and their reports of their programs’ current vacancies. By far the most frequently cited barrier was salary, which was cited by 57% of program managers. Lack of candidates with desired credentials or desired work experience, small applicant pool due to geographic location, and competition from other fields were all cited by more than one quarter of program managers. Program and organization characteristics that were related to multiple perceived barriers included organizational industry, state operation, organizational size, and geographic region. In logistic regression models, salary remained significantly related to industry, with OPHA program managers being less likely to cite salary as a barrier. Likewise, state operation and salary were related, with program managers in state operated organizations more likely to cite salary as a barrier. Program vacancy rates ranged from 0% to 100%, and the median of 4% was used to divide programs into two categories: low vacancy (less than 4%) and high vacancy (greater than 4%). These categories proved to be unrelated to most of the program and organizational characteristic variables. Staffing patterns offered one exception: The mean proportion of RNs in low vacancy programs was slightly but significantly lower than the mean proportion of RNs in high vacancy programs, which could be in part related to the comparatively high rate of vacancies in RN positions, across programs. 33 CHAPTER 4: CURRENT AND FUTURE STAFFING NEEDS The purpose of this chapter is to identify unmet needs for the behavioral healthcare workforce with a focus on type of position. The first section focuses on psychiatrists and other prescribers, primarily advanced practice psychiatric nurses. The second section focuses on other professional and non-professional staff. Each of these sections employs data from different sources so the methods upon which we have relied are described within each section, as well as the implications for higher education. The third section describes one underlying problem, the level of compensation currently available to the Oklahoma workforce. Need For Psychiatrists and Other Prescribers of Psychiatric Medications State mental health authorities typically do not have empirical information about the characteristics of their current workforce. In order to fill this information gap, a number of studies were undertaken, as well as searches for relevant data, that would provide useful information for understanding difficulties faced by staff providing mental health services in Oklahoma. Among the studies that we identified was a study of the relative unmet need for professional mental health workers in the State of Washington (Morrissey, et al, 2007a), undertaken as a part of the Mental Health Transformation State Incentive Grant. Morrissey and his colleagues employed a simple model as the foundation of their work. First, they estimated the number of adults (persons over age 18) who could be classified either as persons with serious mental illness or as persons with other mental health needs. For each of these two types of persons, estimates were developed on the percentages that would access mental health non-inpatient services in one year and the number of units of professional services they would use. Professional services are broken down into those provided by individuals who are licensed to prescribe medications (prescribers) and individuals who are licensed to provide services other than medications (non-prescribers). These estimates then allow new estimates of the numbers of prescribers and non-prescribers needed (in full time equivalents—FTE) to serve a population within a defined geographic area. The estimates of need are then subtracted from the number of licensed professionals available to yield the shortage of professionals. A summary of the model follows: Need = People with serious mental illness + people with other mental health needs Workforce = Prescribers + Non-prescribers Shortage = FTE available – FTE needed It is important to emphasize that these are relative not absolute measures of unmet need. This means that they are most useful in comparing the need from one area to another, but do not necessarily provide an estimate of the exact number of additional professional staff needed. Moreover, apparent surpluses produced by these estimates cannot be relied upon. The study of Washington State was a part of a larger, national study sponsored by the Health Resources Services Administration (HRSA) of the U.S. Department of Health and Human Services. This allowed Morrissey and his colleagues to develop estimates of professional shortages for every county in the U.S. We contacted them and requested estimates for 34 Oklahoma. The findings, as well as the methods employed to arrive at the estimates, are presented here. This also includes some of the limitations of these findings. Findings Most specialty prescribers in Oklahoma are psychiatrists, although there are a handful of advanced practice psychiatric nurses. Other physicians can and do prescribe psychiatric medications, as well. Exhibit 4.1 below presents regional and statewide estimates of counts of prescribers available to provide mental health services in Oklahoma. As previously discussed, the state is divided into six regions, as follows: Central Oklahoma (counties in which Oklahoma City is located) and Tulsa are separately estimated, while.the remaining counties are grouped into four quadrants - northeast, northwest, southeast, and southwest. Counties are grouped because data at an individual county level is often too small to provide reliable estimates. The table shows 278 FTE psychiatrists/prescribers. Exhibit 4.1: Available FTE Mental Health Specialty Prescribers by Licensure Group and by Oklahoma Regions Licensure Region Advanced Practice Psychiatric Nurses (APPN) Psychiatrists Smoothed Total Prescribers20 OKC 10 133 107 Northeast 2 30 78 Northwest 3 8 9 Southeast 3 13 24 Southwest 1 25 38 Tulsa 6 70 32 Total 23 278 287 For psychiatrists, full time equivalents are greater than the raw count because practice pattern data indicate that psychiatrists average more than 40 hours/week. Exhibit 4.2 below presents regional and statewide totals of FTE needed and FTE shortages for prescribers. For the prescriber group, the UNC estimates produce a shortage of 410 FTEs. 20 In the initial analysis, the county is used as the primary geographical unit for shortage estimation. This decision was made primarily due to the lack of accurate small-area data on mental health needs and practice locations, but also because people are likely to travel within larger areas for mental health services. Each county-level need and supply estimate was adjusted using a smoothing method that accounts for travel across county boundaries for mental health services. Within Oklahoma particularly, with its many small counties, ignoring this would lead to overestimates of need. The maximum amount of time that people can be expected to travel for mental health services is about 60 minutes (Fortney, Owen & Clothier, 1999; Fortney, Rost, Zhang et al., 1999). Therefore, for a given index county, the need and supply estimates of counties within a 60-minute radius were weighted and added to the estimates for the index county. The weighted estimates were scaled so that the national need and supply totals for prescribers and non-prescribers were unchanged by the smoothing process. In the final analysis, counties were aggregated by regions within Oklahoma. 35 Exhibit 4.2: Estimates of Shortages of Specialty Behavioral Health Prescribers by Oklahoma Region Region Total FTE Available, Smoothed FTE Needed, Primary Care Adjusted, Smoothed Relative Shortage (FTE), Primary Care Adjusted, Smoothed OKC 107 187 -80 Northeast 78 217 -139 Northwest 9 32 -23 Southeast 24 107 -83 Southwest 38 94 -56 Tulsa 32 61 -29 Total 287 697 -410 Methods Methods are described in detail in Morrissey et al (2007b). Exhibit 4.3 below presents a brief summary of the data sources employed and how the estimates were derived. Exhibit 4.3: Data Sources Employed To Estimate Behavioral Health Workforce Needs and Available Workforce Variable Estimated Source of Data Oklahoma specific data Prevalence of Mental Illness (persons-in-need) National Comorbidity Survey Replication (NCSR); Medical Expenditures Panel Survey (MEPS) Yes Estimates of percent of persons-in-need using mental health services annually MEPS for non-SMI population; Assume 100% for SMI population No Estimates of average units of outpatient services used per person annually NCSR, MEPS No Estimates of visit hours per working day for prescribers Substance Abuse and Mental Health Services Administration (SAMHSA) No Estimates of need met by primary care providers Need estimate reduced by 15 percent in counties without a shortage of primary care providers (no single reference) Yes Estimates of supply of mental health professionals Various sources, generally relevant professional associations Yes Adjustments of need in rural counties that are close to larger counties Various references; assumed maximum travel time would be 60 minutes for mental health services Yes As shown above, Morrissey and his colleagues relied upon a number of data sources in order to estimate each of the variables required to determine workforce shortages. These sources are generally recognized as the most reliable sources of information available, although in several cases these may be the only sources available. 36 Discussion Prior to the completion of the work by Morrissey and his colleagues, the only available estimates of need for mental health professional services were on the website of the Health Resources and Services Administration. However, there was no explanation of the method employed to develop these estimates or references to underlying research. Thus the work described here represents the first systematic attempt to provide appropriate estimates of workforce needs and shortages. Nonetheless, there are limitations that must be recognized. Morrissey et al (2007b) acknowledge that the populations included do not extend to adults who are homeless or in institutions (e.g., inpatient, corrections) or children and adolescents. They also do not include needs for staffing of substance abuse programs. They indicate that the measure of shortage “is probably most useful when taken as an expression of relative, rather than absolute unmet need.” Finally we reviewed the UNC estimates of available FTE with more recent data from Oklahoma State licensing boards. The UNC estimates are generally close, but underestimate the size of the current, licensed workforce. However, there is no data available on whether individuals who are licensed are actually engaged in clinical practice. We know anecdotally that at least some may be retired or only have a part-time practice or are working in administrative, rather than clinical positions. As we have also pointed out above, other licensed individuals are working in positions that are not counted in the need estimates (e.g., agencies serving children and youth, agencies providing adult or child inpatient care). As a result, we believe that the strategy of simply counting licensed practitioners leads to a systematic overestimate of the available supply of such professionals and, therefore, an underestimate of the shortage of prescribers. Addressing the Shortage of Prescribers Oklahoma has three psychiatric residency programs which collectively produce about 13 new psychiatrists per year. Assuming that our estimate of the current need for over 400 prescribers of psychiatric medications is reasonably accurate, it would take over 30 years for these programs to fill the unmet need. This does not account for retirements during this period which will only increase the unmet need. It is unlikely that these residency programs will expand substantially or that psychiatrists will be recruited in significant numbers from elsewhere in the United States because this is a national problem. The numbers of new doctors entering psychiatric residency programs has been falling for over 20 years, and changes that would reverse this trend do not seem likely. Information about Doctors of Osteopathy (D.O.s) either training to practice psychiatry or trained to do so in Oklahoma suggests that this group also is not likely to expand the numbers of prescribers in the foreseeable future. There are no osteopathic residency training programs in psychiatry in Oklahoma, and only a few D.O.s practice primarily psychiatry in Oklahoma (36) and even fewer are certified to do so (18). Given that psychiatrists cannot be expected to fill the need for new prescribers, what options exist? At least three possibilities exist: · Advanced practice psychiatric nurses can be trained to fill this need. At present there are only a handful of persons with this training in Oklahoma, but nursing schools could be encouraged to offer the necessary education. 37 · With an expansion of integrated primary care and behavioral health care, primary care physicians could become an expanded source of prescribers. Creating incentives to develop integrated care practices, targeting training in integrated care, and promoting continuing education in prescribing psychiatric medications, could contribute to an expansion in the numbers of competent prescribers. · Licensing Ph.D. psychologists with special additional training to prescribe medications would also expand the numbers of prescribers; two states now allow this. All three approaches may be necessary to fill the gap, which is quite substantial. If no action is taken to increase the numbers of prescribers, the problem may become worse with the retirements of older psychiatrists, who were trained in an era when psychiatry was a more attractive field. The numbers of retirements may exceed the small numbers of annual replacements. Conclusions The UNC data demonstrate an unequivocal need for more prescribing professionals in all areas of the state. The total estimate of need for 410 additional prescribers is probably an underestimate for reasons discussed above. The area of the State with the greatest unmet need is the northeast quadrant, excluding Tulsa which has the smallest, relative unmet need. Non-Prescribers Exhibit 4.4 below shows current staffing by position type for the state of Oklahoma and the six regions within the State. Then, exhibit 4.5 below shows population-based rates for behavioral healthcare positions by type in Oklahoma and the surrounding states. Oklahoma and the surrounding states are similar in most categories. The major exception is RNs which are less available in Oklahoma. LPNs and MH/SA Techs are marginally more available. There is considerable variation in the availability of behavioral health care jobs within Oklahoma. The central region (OKC) has among the highest rates of availability for all categories of positions. Tulsa is close and leads in availability of MH/SA Techs. The more rural areas of the state have significantly less availability of professionals, psychologists, MH/SA Counselors, and RNs. 38 Exhibit 4.4: Current (2008) Numbers of Behavioral Healthcare Positions by Positions Type in Oklahoma ODMHSAS Region Counts Position State Tulsa OKC NE NW SE SW Psychologist 1,339 195 628 236 58 96 126 MH/SA Counselor 6,993 1,100 2,691 1,485 281 785 651 RN 26,157 5,714 10,839 3,560 962 2,766 2,316 LPN 13,463 2,411 4,163 2,062 716 2,100 2,011 MH/SA Tech 38,590 9,124 11,394 6,474 1,833 5,530 4,235 Exhibit 4.5: Current (2008) Rates per 10,000 Population of Behavioral Healthcare Positions by Position Type in Oklahoma and Surrounding States ODMHSAS Region Rates Position Okla-homa Rate Multi- State Regional Rate* National Rate Tulsa OKC NE NW SE SW Psychologist 3.9 8.1 9.6 3.5 6.3 3.1 2.9 2.1 2.8 MH/SA Counselor 20.3 27.0 34.4 19.5 27.2 19.2 14.0 16.8 14.2 RN 75.8 87.0 92.7 101.4 109.5 46.1 47.8 59.4 50.6 LPN 39.0 35.0 27.5 42.8 42.1 26.7 35.6 45.1 43.9 MH/SA Tech 111.8 121.3 126.1 162.0 115.1 83.8 91.1 118.7 92.5 Regional rate includes the following states: Arkansas, Colorado, Kansas, Louisiana, Missouri, New Mexico, Oklahoma and Texas. Exhibit 4.6 below shows the current (as of 2008) numbers of behavioral healthcare staff by position type for Oklahoma and for the United States overall. It also shows the projected needs for staffing as of 2018. Projections are based principally upon projected population changes, and to a lesser degree on additional factors, described in the Job Growth section below. The additional positions are necessary to maintain the same rates of services currently provided. 39 Exhibit 4.6: Oklahoma and National Current (2008) and Projected (2018) Rates of Change for Behavioral Healthcare Staffing Positions Oklahoma National Position 2008 Jobs 2018 Jobs Change % Change % Change Psychologist 2,738 3,099 361 13.2% 15.8% MH/SA Counselors 9,726 11,377 1,651 17.0% 17.8% RNs 26,552 32,271 5,719 21.5% 22.3% LPNs 13,936 15,554 1,618 11.6% 12.9% Aides/Techs 44,546 54,536 9,990 22.4% 23.8% Total 97,498 116,837 19,339 19.8% 21.3% The difficulty is that these estimates include only job growth in predicting the numbers of new persons needed to fill available positions. However, current members of the behavioral healthcare workforce will be leaving their positions, either for retirement or other reasons. This also needs to be accounted for in developing estimates of persons needed to fill positions annually. Next, an approach employing appropriate data to reach this goal is outlined. Job Growth The table above from the Department of Commerce shows that in 2018 there will be 361 more jobs for psychologists than there were in 2008. One implication is that it is necessary to train or import 361 new psychologists into the system between 2008 and 2018. The source of these estimates is data from Economic Modeling Systems, Inc (EMSI). EMSI uses several different databases to come up with their estimates, including population projections from the Census Bureau. However, that is not the only factor, current employment trends and participation rates from the Bureau of Labor Statistics (BLS) go into the projections. Also included are Internal Revenue Services income and migration data that shed more light on the single employers or those that do not pay into Unemployment Insurance. The simple explanation is that it uses current employment trends by industry and certain population trends. Industry trends, legislation, and several other factors are also used to decipher which industries will be growing. Population is a key component but labor participation, county wages, migration patterns and trading patterns are also factors that influence the model. EMSI breaks down these trends to the county level, which can then be aggregated to the state level. The next table shows the rate of growth for ten years, which translates into an annual growth rate between one and two percent, depending upon the position type. 40 Exhibit 4.7: Ten Year Growth by Position Category Position Category State 2008 Jobs 2018 Jobs % Change Psychologist 2,738 3,099 13.2% Mental Health or Substance Abuse Counselors 9,726 11,377 17.0% Registered Nurses 26,552 32,271 21.5% Licensed Practical Nurses 13,936 15,554 11.6% Mental Health or Substance Abuse Aides/Assistants/ Technicians 44,546 54,536 22.4% Overall Total 97,498 116,837 19.8% This assumes that the persons occupying these positions in 2008 continue to be available to fill positions in 2018. However, this is not the case. Some people who occupy positions in 2008 will retire or leave the field for other reasons over the next ten years. For example, it will be necessary to train or import more than 361 new psychologists into the system for these reasons. The problem is to estimate how many more psychologists will be needed to fill available positions. Accounting For Persons Leaving the Field Let us assume that from 2008 to 2009 the growth rate for psychologists is two percent. Then the change in jobs would be an increase of 55, meaning that there would be a need for a minimum of 55 new psychologists to fill those jobs. In addition, let us assume that there is a 20 percent separation rate among psychologists during 2008 or 550 persons and further that ten percent of those separated actually leave the field. That would mean an additional 55 new psychologist would be needed to fill those vacated jobs, giving a total of 110 psychologists needed to fill the new jobs and the jobs vacated by those leaving he field. If this reasoning is correct, then modeling the number of new persons needed to fill psychology jobs requires an annual estimate of the percent of persons leaving the field. As a part of our survey work, we collected information from 1,349 individual staff who are currently in behavioral healthcare positions. We asked each of those individuals to indicate whether they planned to stay in their position during the next year. Twenty one percent of staff and six percent of program managers indicated that they plan to leave their positions. The percentages of persons indicating that they planned to retire or indicating that they planned to leave behavioral healthcare are shown in the following table. 41 Exhibit 4.8: Staff Planned Separation Rates and Program Manager Estimated Separation Rates (1) (2) (3) (4) (5) (6) (7) Position Type Persons answering survey Percent planning on separating within the next year Percent planning to retire Percent planning to leave the field Sum of columns (4) and (5) Estimated separation rates from program manager surveys Aide/tech 343 21% 0% 6% 6% 42% Masters-level professional 317 19% 1% 3% 4% 26% LPN 37 24% 3% 8% 11% 35% Psychiatrist/ physician21 - - - - - 23% Psychologist 28 21% 4% 0% 4% 11% RN 149 19% 1% 5% 6% 26% Total 874 20%22 1% 4% 5% 35% Note that, with the exception of psychologists, program manager-reported, actual separation rates exceed staff self-reported plans to separate by a wide margin. However, these data can be considered together to estimate a range of possible industry departure rates. At the conservative end of the range is the staff self-report: An overall rate of 5%, with position-type specific rates ranging from 4% to 11% is probably a conservative estimate of rates of person who separate leaving the field. Alternatively, the proportion of planned industry departures can be applied to the program manager-reported separation rates for a less conservative estimate. These range from a low of 11% for psychologists to a high of 42% for the aide/tech positions. These rates are higher than the annual growth rates projected by EMSI. This means that the growth in estimates of persons needed to fill positions year by year will be influenced to a much greater degree by estimates of staff turnover, representing the need to replace existing members of the workforce. There is one additional consideration in estimating the numbers of new persons needed to fill positions. The “jobs” in the EMSI estimates are only filled jobs; they do not include unfilled jobs. Thus, there is a need to take into account vacancy rates for the appropriate position type. The estimates of vacancy rates for Oklahoma for each position type are shown in Table 4.9. 21 There is insufficient data for psychiatrists to provide these estimates. 22 Note that this is very slightly lower than the rate cited in the text above (21%). The information in this table is based only on responses that could be linked to a position type (N=877), while the overall number cited in the text is based on all the responses to this item that were received (N=965). 42 Exhibit 4.9: Vacancy Rate by Position Category Position Type Percent Vacant Aide/tech 9% Masters-level professional 17% LPN 10% Psychiatrist/ physician 10% Psychologist 6% RN 14% Total 12% With this term included, for each position type the number of new persons needed to fill existing vacancies, positions vacated by persons leaving the field and by growth (from EMSI) would be given by the following equation: [new persons needed in year i+1] = [number of jobs in year i] x [growth rate + percent leaving field] x [1- vacancy rate] Findings Findings are presented for three position types: psychologists, mental health and/or substance abuse counselors, and mental health and/or substance abuse aides/techs. The latter are direct care positions that do not necessarily require professional degrees or licensure. We have not included registered nurses and licensed practical nurses because only a relatively small proportion of these positions are in behavioral healthcare and separate estimates of need have been developed by the Oklahoma Healthcare Workforce Center. The need for psychiatrists and other prescribers is discussed earlier in this section. The “net growth” figures in the column to the right show the numbers of additional persons who must either be trained or imported over a ten year period to be sure that the estimated behavioral healthcare positions for 2018 and in the intervening years will be filled. This is 1,808 psychologists, 7,045 mental health and substance abuse counselors, and 51,625 aides or techs. Exhibit 4.10: Net Growth by Position Category State Position Category 2008 Jobs 2018 Jobs 2018 Persons Needed Net Growth Psychologists 2,738 3,099 4,546 1,808 Mental Health or Substance Abuse Counselors 9,726 11,377 16,771 7,045 Mental Health or Substance Abuse Aides/Assistants/ Technicians 44,546 54,536 96,171 51,625 43 Estimates shown in the table above are conservative for the following reasons: · Separation rates are estimated from individual reports of intentions to leave their present positions, rather than the separation rates estimated from program manager reports of persons leaving their positions. The latter are two to four times higher than the former. · The number of positions only includes those who are considered “state covered.” If all positions including persons in individual or small group private practices are included the numbers would also be higher. This is particularly true for psychologists who are much more present outside the public sector than inside. Exhibit 4.11 below shows the numbers of degrees awarded each year over a six year period beginning in 2001-02 and ending in 2006-07. (A detailed breakdown of degrees awarded in specific fields within each of these larger categories is provided in Appendix A15.) With the exception of psychologists, there has been an increase in each category over this time period. The two columns at the right of the table show the number of degrees expected to be awarded cumulatively from 2007-08 through 2017-18 and the need for new degree recipients to meet the demand for new staff positions. The projections of degrees awarded are based upon a simple linear trend model employing the six years of recent data available on degrees awarded. The model may be underestimating the number of psychology degrees to be awarded, in particular. Exhibit 4.11: Degrees Awarded By Public Higher Education Institutions 2001-02 to 2006-07 and Projected to 2017-18 Compared to Projected Need Position Category 2001-02 Degrees 2002-03 Degrees 2003-04 Degrees 2004-05 Degrees 2005-06 Degrees 2006-07 Degrees Cumulative Degrees Projected through 2017-18 Cumulative Projected New Need by 201823 Psychologist 50 64 51 41 44 44 204 1,808 Mental Health or Substance Abuse Counselors 374 375 391 409 360 421 4,478 7,045 Mental Health or Substance Abuse Aides/Assistants/ Technicians24 1,122 1,090 1,129 1,203 1,208 1,262 14,913 51,625 The number of new cumulative degrees projected by 2017-18 consistently falls short of the cumulative projected new need of persons by 2018, as calculated in Table 4.10. This is further exacerbated by the fact that Higher Education data demonstrates that five years after graduating from Oklahoma with a behavioral health degree only 49% are employed in Oklahoma within a behavioral health care field, although the number of persons qualified in these fields that enter into Oklahoma in a given year is unknown. 23 This estimate does not include individuals needed to replace persons in existing positions who retire or leave the behavioral healthcare system. 24 For these positions, we counted individuals with bachelor’s degrees in social science fields. 44 Conclusions The difficulties experienced by program managers of behavioral healthcare services in recruiting staff to fill vacancies are expected to become more complicated in the coming years. For both professional and non-professional staff the numbers of new persons being trained to account for both persons leaving existing positions and the limited expansion anticipated are not keeping pace with the need, even based on conservative estimates. Compensation Earlier in this report, survey data were presented on the reasons why programs have high staff turnover rates and difficulties recruiting new staff. Across all position types and across almost all industry groups the single leading explanation is low salaries. Further survey data were presented from individual staff on their salaries. A second source of data was utilized on staff salaries by position type, as well as national comparisons, comparisons to surrounding states, and comparisons within areas of Oklahoma. The source of these data is EMSI. Findings Findings are presented for five position types, psychologists, mental health and/or substance abuse counselors, registered nurses, licensed practical nurses, and mental health and/or substance abuse techs. The latter are direct care positions that do not require professional degrees or licensure. Data for psychiatrists is not separately available in the EMSI data set. Data are further presented for the state of Oklahoma overall and for six regions within the state. Exhibits 4.12 and 4.13 below present comparisons of wages. For all positions wage rates for Oklahoma are consistently below both national and regional averages. However, the disparity between Oklahoma and national wage rates is larger than the disparity with regional wage rates. Within Oklahoma, there is also variation among the six regions. In general, wages are among the highest in the Tulsa area for all position types except psychologists. The Central (Oklahoma City) region also tends to have higher rates than the other regions. Among the four regions with rural counties, there is no region that is consistently among the highest or the lowest. For two position types there is considerable regional variation. Psychologists range from a high of $31.72 per hour in the Southeast region to a low of $23.66 in Tulsa, a difference of 25%. MH/SA Counselors range from a high of $19.28 per hour in Tulsa to a low of $13.61 in the northwest, a difference of 33%. All other variations are less than 15%. 45 Exhibit 4.12: Comparison of Average Hourly Wage: National, Regional and Oklahoma Norms for Behavioral Healthcare Positions by Type *Regional rate includes the following states: Arkansas, Colorado, Kansas, Louisiana, Missouri, New Mexico, Oklahoma and Texas. Exhibit 4.13: Comparison of Average Hourly Wage: Oklahoma Norms for Behavioral Healthcare Positions by Type ODMHSAS Region Rates Position State Rate Tulsa OKC NE NW SE SW Psychologists $25.74 $23.66* $29.03 $30.47 $25.23* $31.72 $24.56* MH/SA Counselors $15.12 $19.28 $18.36 $15.67 $13.61* $15.57* $16.10* RNs $24.52 $25.25 $24.96 $22.39 $23.63 $21.34 $24.45 LPNs $15.55 $16.48 $16.06 $14.09 $14.73 $13.70 $14.58 MH/SA Techs $12.35 $12.74* $13.01 $11.37 $11.87* $11.64 $11.97 *Rates may vary due to missing values. Summary It is clear that salary rates for all positions are lower in Oklahoma than in the nation and further that Oklahomans filling these positions providing behavioral healthcare are paid less than individuals in all of the surrounding states. There is also some variation within the State. For the two position types that have the largest numbers of persons providing behavioral healthcare, MH/SA Counselors and MH/SA Techs, salaries are higher in the Oklahoma City and Tulsa areas than they are in the more rural northeast, northwest, southeast, and southwest quadrants of the state. Overview of Current and Future Needs for Behavioral Healthcare Workforce As indicated elsewhere in this report, behavioral healthcare programs have difficulty retaining and recruiting staff. There is a very large gap in the need for psychiatrists and other prescribers. Currently, it is estimated that there is a need for 697 prescribers and only 287 professionals available to meet the need, a difference of 410. While the unmet needs for other professionals and non-professionals are not as large proportionately, there are gaps in these position types as well. Additionally, the rates at which institutions of higher education in Oklahoma are producing new graduates with appropriate training are not sufficient to meet these needs, particularly with projected future growth of these positions. Furthermore, attracting new individuals into service Position State Rate National Rate Multi-State Regional Rate* Psychologists $25.74 $30.27 $26.76 MH/SA Counselors $15.12 $18.63 $16.43 RNs $24.52 $30.06 $26.98 LPNs $15.55 $19.51 $17.53 MH/SA Techs $12.35 $14.02 $12.94 46 or training is significantly handicapped by the fact that salaries for both professional and nonprofessional positions in Oklahoma are consistently lower than the surrounding states and the nation, as a whole. 47 CHAPTER 5: BENEFITS & COMPENSATION Information on benefits and eligibility practices was collected via the organizational survey. Organizations were categorized as either state operated or private. Given the commonly-held perception that the state benefit package is preferable to benefit packages for employees of private organizations, it is useful to compare benefit packages offered by these two types of organizations. Benefits Provided Organizations provided information on the types of benefits they offer employees, and on the specifics of their healthcare coverage. Exhibit 5.1 shows the percentages of state operated and private organizations offering each type of benefits. Seventeen state operated and 97 private organizations responded to these items. Exhibit 5.1: Proportion of Organizations Providing Benefits* Data from the organizational surveys. The commonly held belief that state employers offer more comprehensive benefits packages than private employers is supported by the data. All state operated organizations offer full-time employees health, life, dental and disability insurance, a flexible spending account and a wellness program. Health insurance is offered by almost as many privately operated organizations (95%) as state operated, but coverage decreases with each benefit thereafter (life insurance provided by 85%; dental insurance provided by 75%, and disability insurance provided by 62%). 48 On the organizational survey, a representative from each organization was asked to provide the percentage of their employees covered by insurance, as well as the percentage of full time employees’ insurance costs covered by the organization. Exhibit 5.2 shows the average of the 17 state operated and 87 private organizations’ responses to these items. Nearly all state workers were reported to be insured, while just under three quarters of staff working for privately operated organizations had insurance. State operated organizations reported covering all insurance costs for their employees, while privately operated organizations covered an average of 84% of the cost of their employees’ insurance. Exhibit 5.2: Proportion of Staff Receiving Health Insurance and Proportion Costs Covered Data from the organizational surveys. Staff Satisfaction with Benefits Staff were asked to respond to a series of questions about their satisfaction with the benefits their organization offered. For each item, staff were asked to choose one of six responses: very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, very dissatisfied, or no basis to judge. Exhibit 5.3 displays staff responses to questions regarding different benefit types; 1,178 staff responded to at least one of these items. For the purposes of analysis, staff responding that they were either very satisfied or satisfied with a particular benefit were considered to be satisfied. Overall, nearly three quarters of staff surveyed reported being satisfied with the paid leave and paid vacation time they receive. There is less satisfaction with retirement benefits (51%) and health insurance (56%), and the least satisfaction is associated with options for Flexible 49 Spending Accounts (FSAs; 45%) and child care subsidies/other child care options (25%). As noted earlier in this section, both state and privately operated organizations are very unlikely to offer staff child care options. This could ultimately take a toll on the work-family life of employees and impact their overall job satisfaction. Exhibit 5.3: Staff Satisfaction with Benefits Data from the staff surveys. Industry Group and Satisfaction with Benefits Staff satisfaction with benefits varied by industry group and organization type (state operated vs. privately operated). The Child Guidance and DOC industry groups were composed entirely of state operated organizations that typically offer better benefits packages compared to private organizations. Therefore, it is not surprising that staff from these two industries were more likely to be satisfied with the FSAs, health insurance, paid leave for illness/family care, and paid vacation time offered at their organizations. Satisfaction with child care benefits did not vary significantly by industry group, and therefore does not appear in Exhibit 5.4. 50 Exhibit 5.4: Staff Satisfaction with Benefits by Industry Satisfaction with… MH OPHA OJA DOC SA ChildG Health insurance (N=1024) 61% 53% 69% 81% 63% 76% Flexible Spending Account (N=838) 51% 42% 44% 61% 38% 79% Retirement benefits (N=1026) 61% 55% 63% 57% 43% 78% Paid vacation time (N=1035) 75% 69% 86% 95% 80% 95% Paid leave (N=1023) 74% 66% 89% 91% 79% 95% Data are significant at the p<.05 level. Health Insurance Coverage and Satisfaction with Benefits For the purposes of examining the relationship between health insurance coverage and staff satisfaction with benefits, organizations were grouped according to the proportion of health insurance costs covered. As shown in Exhibit 5.5, staff employed by organizations in the high coverage (100% of health insurance costs covered) were more satisfied with their benefits than staff employed by organizations in either of the other two groups. One exception to this finding was satisfaction with child care options, which was similar among staff from the high coverage and medium coverage (80-90% of costs covered) groups. Exhibit 5.5: Percentage of Health Insurance Covered by Staff Satisfaction with Benefits Satisfaction with… Employer Covers <78% N=115 Employer Covers 80-90% N=102 Employer Covers 100% N=228 Child care options 11% 27% 25% Flexible Spending Account (FSA) 34% 46% 53% Health insurance 36% 68% 73% Retirement benefits 30% 62% 65% Paid leave for illness & family care 66% 77% 87% Paid vacation Time 71% 78% 85% Data from the staff and organizational surveys. ♦ Data are significant at the p<.05 level. ♦ Staff is the unit of analysis. Staff Pay As discussed in the separations section, staff were asked to indicate their hourly wage range using a multiple choice question with $5.00 per hour increment pay ranges beginning at less than $10.00 per hour and ending at $50.00 or more per hour. For the purposes of analysis, the responses to these items were transformed into scale data using the midpoints of the pay increments. Details on the overall distribution of this variable are shown in Appendix A9. Exhibit 5.6 shows the distribution of the original pay categories. As might be anticipated, the responses were heavily clustered in the more modest pay categories. Over half of responding staff earned less than $15.00 per hour, with close to one-fifth making less than $10.00 per hour. Oklahoma uses the federal minimum wage, which increased from 51 $6.55 to $7.25 per hour during the data collection period for this study. Given that ninety-two percent of staff reporting wages of less than $10.00 per hour also reported being employed full-time, the minimum wage rate and upper limit of this wage category can be used to create an estimated gross annual income range of $14,500.00 to $20,000.00 for the majority of staff in this category (those employed full-time). Staff earning towards the upper end of the range are at 185% of the 2009/2010 poverty guidelines if they have no dependents, but are under the poverty line if they have more than two dependents. Staff earning towards the lower end of the range are at roughly 133% of the 2009/2010 poverty guidelines if they have no dependents, but are under the poverty line if they have any dependents (Office of the Assistant Secretary for Planning and Evaluation, 2010). Exhibit 5.6 Staff Pay Distribution Staff pay varied by position type, as expected. Exhibit 5.7 shows the mean hourly wage as well as the lowest pay range and highest pay range selected for staff in five position categories. Physicians are not included in this table due to the small number of physicians responding to the staff survey. Psychologists reported the highest hourly wages, but those reported by Registered Nurses were fairly similar. Licensed Practical Nurses had a mean hourly rate relatively close to that of counselors, most of whom had Masters of Social Work or other Masters degrees. Techs reported the lowest wages, with an average of $11.23 per hour. 52 Exhibit 5.7: Wage by Position Type Mean Hourly Wage Lowest Wage Reported Highest Wage Reported Aide/tech (N=385) $11.23 Less than $10.00 $20.00 - $24.99 Masters-level professional (N=469) $18.64 Less than $10.00 $50.00 or more LPN (N=40) $16.38 $10.00 - $14.99 $20.00 - $24.99 Psychologist (N=12) $28.33 $15.00 - $19.99 $35.00 - $39.99 RN (N=124) $26.71 Less than $10.00 $40.00 - $44.99 Overall (N=1003) $17.03 Less than $10.00 $50.00 or more Mean wages based on midpoint of pay range selected ♦ Data from the staff survey. Relationship between Pay and Program and Staff Variables The relationship between staff pay and a variety of program characteristics and staff variables was examined. First, it was determined whether there were relationships between staff pay and the study dimensions variables described in earlier sections.25 Then relevant staff variables were considered: staff member race (American Indian/Alaskan Native, Asian, Black, Native Hawaiian/Pacific Islander, White and more than one race), ethnicity (Hispanic/Non-Hispanic), gender, age, current position tenure, organizational tenure, years in behavioral healthcare field, highest degree achieved (high school/GED, Associates/two-year degree, Bachelors/four-year degree, Masters degree, doctoral degree, and medical degree) and current position title (reported using the study’s six position-type scheme, described earlier). Tenure items were reported in years.26 Analysis and Results The relationships between staff pay and each of the variables above were tested in a linear regression model. 27 Of the program variables, service type, consumer population age, and organizational size remained significant in the regression; staff position was the only staff 25 Study dimensions variables - industry group, geographic region, program service type, service setting, age range of consumer population, state operated status, and organizational size. 26 As noted in the separations section, age was reported using age range categories, but for the purposes of analysis, the midpoint of these ranges was used. Given the very small number of physicians responding to the survey, this position type category was eliminated from the analysis. 27 A number of variables were excluded due to concerns about correlation with other predictors. These included industry group, years in position, years in field, and education. As both members of correlated variable pairs were generally strongly associated with pay, the exclusion decisions were based on the perceived utility of the variables. Additionally, two variables were excluded because their association with pay, while significant, was unexpected and difficult to interpret as anything other than the result of a relationship with another predictor variable. First, while the Oklahoma City metro area’s position as the region with the highest mean hourly wage ($19.08) was not surprising, the Tulsa metro area’s mean wage was unexpectedly much lower ($16.86) and was also much lower than that for the southeast quadrant ($18.57). We believe this is at least partially a result of the relationship between region and industry. Additionally, the southeast quadrant’s unexpectedly high average wage may be related to the small but still disproportionately high number of doctoral-level clinicians reporting from this region, as well as the slightly high proportion of counselors/Masters-level professionals. Second, the average hourly wage for women was over two dollars higher than that for men ($17.50 versus $15.46). We attribute this to the relationship between gender and position type. While men made up roughly one quarter of the staff responding to the survey overall, nearly two-fifths of the staff in the lowest-paid position category (aids/techs) were male, and only 15% of the staff in the highest-paid of the well-populated position categories (RNs) were male. 53 variable that did so. The mean hourly wage and lowest and highest wage ranges for staff in each of the four service type categories are shown in Exhibit 5.8. Mental health staff had the highest mean hourly wage at $17.41 per hour, over six dollars per hour higher than the staff in the lowest-paid service type, developmental disabilities and mental health or substance abuse care. In the regression the significance of service type resulted from the difference between mental health staff (the reference category) and staff in programs serving people with co-occurring developmental disabilities and behavioral health needs as well as the difference between mental health staff and substance abuse staff. The pay difference between mental health staff and staff in programs providing both mental health and substance abuse services was not significant. Exhibit 5.8: Pay by Program Service Type Mean Hourly Wage Lowest Wage Reported Highest Wage Reported Mental Health (N=372) $17.41 Less than $10.00 $50.00 or more Substance Abuse (N=70) $15.10 Less than $10.00 $25.00 - $29.99 Co-occurring Mental Health & Substance Abuse (N=303) $16.96 Less than $10.00 $50.00 or more Co-occurring Developmental Disabilities & Mental Health or Substance Abuse (N=57) $11.23 Less than $10.00 $25.00 - $29.99 Overall (N=802) $16.60 Less than $10.00 $50.00 or more Mean wages based on midpoint of pay range selected ♦ Data from the staff and program manager surveys. Mean hourly wages and pay ranges for staff in each of the three consumer population |
Date created | 2011-07-27 |
Date modified | 2011-07-27 |
Tags
Add tags for Oklahoma Behavioral Healthcare Workforce Study Final Report 6 21 11