Claus.ahsr 2009.turnover And Cod Capability Change

Staff Turnover and Change in Co-Occurring Capability Ron Claus, Steven Winton, Mary E. Homan, and Edward Riedel Missouri Institute of Mental Health Addiction Health Services Research Conference October 28 – 30, 2009 Participating Programs

Background Annual turnover rates for public sector behavioral health programs, an often overlooked concern, are estimated to range from 25-50% (Gallon et al, 2003; Glisson et al, 2006; McLellan et al, 2003). Little research has examined the consequences of turnover on an organization, while a majority of turnover research has focused on antecedents of voluntary turnover such as climate, culture, or leadership. High voluntary turnover is typically assumed to be dysfunctional, with organizational consequences that may include:

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18 mental health programs and 9 substance abuse programs providing services to adults.

Characteristic

Mean

Agency Age

27.7 years 8.7

4 – 41

Agency Annual Operating Expenses

$10.6M

$9.7M

$1.9 – 34.6M

Clients below Federal Poverty Level

77.4%

24.5%

19.6 - 100%

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Increased costs due to hiring and training new employees  Reduced productivity, inconsistent services, poor staff morale among stayers  Loss of high performers, loss of institutional knowledge

Range

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Staff reported that changes had largely positive or neutral effects on COD implementation: In each staff category, turnover was viewed as unrelated to value differences regarding COD treatment. Program Size: Clinical & Administrative staff Mean = 38.7, Median = 24, SD = 34.5  Varied widely, from 5-137 employees

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Measuring Rurality: Rural-Urban Commuting Area Codes, USDA, 2007

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Turnover was calculated by dividing the number of clinical and administrative staff who left the program between DDCAT administrations by the number of staff at Year 1. Time between visits varied between programs (11-15 months), so an Annualized Turnover Rate was calculated. Annualized Turnover Rate Mean = 22.5%, Median = 15.0%, SD = 20.6%  Ranged widely, 0 – 94.5% 

Measure: Staff Changes Inventory

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Despite a growing body of research on the translation of research to practice, the effect of turnover on the implementation of evidence-based practices has received little attention.

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1. Describe staff turnover at 27 behavioral health programs 2. 3.

implementing integrated treatment for co-occurring disorders Examine the relationship between turnover and change in cooccurring capability Explore whether organizational characteristics such as program size affect the relationship between turnover and change in cooccurring capability

TEMPLATE DESIGN © 2008

www.PosterPresentations.com

Year 1 Year2

3

2

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2 programs improved substantially (change > X+2SD) 2 programs had lower scores at Year 2

Dual Diagnosis Capability in Addiction Treatment (DDCAT) Index Dual Diagnosis Capability in Mental Health Treatment (DDCMHT) Index – Gotham et al., 2009 Semi-structured questions to elicit ratings on 35 items across 7 subscales:    

Program Structure Program Milieu Clinical Process: Assessment Clinical Process: Treatment

Programs received domain and global scores along a continuum:  Addiction Only or Mental Health Only Services (AOS/MHOS, 1) 

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Programs that by choice or lack of resources cannot accommodate clients with co-occurring disorders, no matter how stable the illness and however well-functioning the client

Dual Diagnosis Capable (DDC, 3) 

Programs that have a primary focus on one disorder but are capable of treating clients who have relatively stable diagnostic or sub-diagnostic cooccurring problems

Dual Diagnosis Enhanced (DDE, 5) 

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Hierarchical Regression Predicting COD Capability Change from Year 1 to Year 2 SE

β

-.690

.192

Negative 20 10 3.8

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.074 -.011

.005

-.586*

Turnover

-.016

.011

-.504

Step 3

p < .01**, p < .05*

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-.572**

Program Size

Program Size X Turnover

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ΔR2 .372**

Step 2

Effect on Implementation (%) Neutral 60 30 61.5

B

Step 1 COD Capability (Yr 1)

Results: Program Turnover Positive 20 60 34.6

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Programs who hired a new Change Agent during the year had less improvement in COD capability than others (0.54 vs. 0.89, d = 0.5). Controlling for initial COD capability, this inverse relationship accounted for 7.8% of COD capability change (F(1, 24) = 3.41, p < .10). Neither annualized turnover rate nor program size directly affected COD implementation. However, turnover interacted with program size to predict change in COD capability (see Figure). Smaller programs with low turnover made noticeably more progress than did larger programs with low turnover (d = 1.40).

Variable

Programs that are designed to treat clients who have more disabling or unstable co-occurring disorders

Category (# programs) Change Agent (n=5) Key Personnel (n=12) Front-line Staff (n=26)

Results: Turnover & COD Capability Change

 Continuity of Care  Staffing  Training

Based on the American Society of Addiction Medicine’s Patient Placement Criteria (ASAM-PPC-2R)

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Study Aims

4

McGovern, Matzkin, & Giard, 2007

14 programs awarded 3-year grants in Dec 2006  13 programs awarded 3-year grants in June 2007

The Co-Occurring Capability of each program was assessed at Year 1 and Year 2. Staff changes and their relationship to co-occurring capability change during that period were investigated.

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Measure: Co-Occurring Capability

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Mean DDCAT Change = 0.82 (SD = .67)

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Study Context: The Missouri Foundation for Health’s Co-Occurring Disorders Priority Area An initiative to support the implementation of evidence-based practices for co-occurring substance use and mental health disorders Publicly-funded treatment providers received support for system change:

Discussion

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What was the overall effect of the change on the co-occurring program? (positive, neutral, or negative)  Was the change related to differences in values or beliefs about treatment for co-occurring disorders? (not at all or a little; some; a lot)

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Results: Co-Occurring Capability

Interview conducted with Change Agent or Program Director Asked whether the program had turnover in three staff categories: Change Agent, Key Personnel (as defined by the program), and Front-Line Staff. If yes, 

Woltmann et al (2008) found that IDDT fidelity scores at follow-up were inversely related to turnover, with most (71%) treatment teams reporting that turnover negatively affected implementation.

Turnover can be either functional or dysfunctional and its influence on EBP implementation may be nonlinear, e.g., as turnover reaches high levels, it may have a decreasing effect on performance. Recent findings suggest that turnover’s impact on behavioral health programs may be moderated by factors such as program size, clinical supervision, and training infrastructure.

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Turnover and Program Size Predict Co-Occurring Capability Change

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Urban Core: 3 SA providers, 11 MH providers, 51.9% Large Town: 4 SA providers, 6 MH providers, 37.0% Small Town: 1 SA provider, 1 MH provider, 7.4% Isolated Small Census Tract: 1 SA provider, 3.7%

Turnover, however, can also be classified as functional. Lesserstudied organizational consequences may include: Displacement of poor performers and improved service delivery  Infusion of new ideas, the stimulation of policy and practice changes , and increased ability to adapt to environmental concerns  Better overall “person-organization” fit

SD

Most located in urban areas: 

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Results: Program Turnover

Acknowledgements .047

.0004

.0003

.597

Average turnover at participating substance abuse and mental health programs was slightly lower than observed in previous national reports. In less than two years, the programs made substantial progress in building capacity to deliver treatment for co-occurring disorders. Treatment staff reported that the consequences of turnover were largely positive or neutral, commonly noting that incoming replacements “had better skills” or were “more involved” in COD treatment. Turnover was not linearly related to change in COD capability. In this sample, turnover may have had mixed positive and negative effects. Small programs improved more when turnover was low, while large programs made bigger strides when turnover was high. This demonstrates differences in the way change occurs at large and small programs. Small programs, which have fewer and more concentrated resources, may be more affected when staff leave. Large programs offer more complex systems but often have more resources; turnover may lead to more rapid improvement, perhaps through identifying, hiring and training more able employees. Programs that brought on new Change Agents improved, but noticeably less than those with consistent implementation managers. This finding suggests the importance of leadership functions in creating program change. Although the current study relies upon a small agency sample, it examines real-life change processes in community-based programs. The study findings highlight the importance of tailoring hiring practices to established COD and management competencies. Future work will examine the role of leadership, staff attitudes toward EBPs, and organizational readiness to change in turnover and program change.

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Support for this presentation was provided by the Missouri Foundation for Health, a philanthropic organization whose vision is to improve the health of the people in the community it services.