Oklahoma behavioral healthcare workforce study : final statewide report.

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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.
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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
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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
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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,
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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
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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.
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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
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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.
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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.
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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.
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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).
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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.
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· 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.
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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.
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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.
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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.
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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