Skip to main content
Full access
Brief Reports
Published Online: 29 October 2019

Characteristics and Job Stressors Associated With Turnover and Turnover Intention Among Community Mental Health Providers

Abstract

Objective:

This study aimed to examine the provider characteristics and job stressors that are related to turnover intention and actual turnover among community mental health providers.

Methods:

Secondary analyses were conducted with data collected from 186 community mental health providers from two agencies. Self-reported provider characteristics, job stressors, and turnover intention data were collected with the baseline survey, and actual turnover data were obtained from the agencies 12 months later. Bivariate analyses were conducted to examine factors associated with each turnover variable.

Results:

Turnover intention and actual turnover were correlated, yet a distinct set of variables was associated with each outcome. Namely, job stressors were related to turnover intention, while provider characteristics were related to actual turnover.

Conclusions:

Given that both turnover intention and actual turnover have important implications for both providers and agencies, it is critical to consider differential factors associated with each.

HIGHLIGHTS

Turnover intention and actual turnover were correlated, yet different factors were related to each outcome.
Job stressors were related to turnover intention, and provider characteristics were related to actual turnover.
High turnover rates of mental health providers, ranging from 25% to 60% annually, represent a significant problem and contribute to provider shortages (1). Turnover can have negative effects on both providers (e.g., reduced well-being) and agencies (e.g., loss of accumulated provider experience). Furthermore, provider vacancies could disrupt continuity and quality of care (2). Identifying and addressing factors that affect turnover are critical steps; yet, research on these topics in mental health settings is scarce.
In the literature, meta-analyses have suggested several predictors of general employee turnover, including individual differences, such as demographic characteristics (e.g., age, gender, education) (3), and job factors, such as job satisfaction, organizational commitment, and burnout (4). Although limited, research has also examined turnover factors specifically in mental health settings. For example, Beidas et al. (5) identified individual- and organizational-level factors predicting turnover (e.g., burnout, job attitudes, and personal, organizational, and financial reasons) among staff implementing evidence-based practices in a public mental health system.
In predicting turnover, studies commonly employ turnover intention (i.e., thoughts about leaving a job) as a proxy for actual turnover. The use of turnover intention is often justified on the basis of the strong correlation between turnover intention and actual turnover (1). Additionally, both turnover intention and actual turnover are considered important because of their impacts on other job outcomes. For example, increased turnover intention may be associated with reduced job commitment and frequent absenteeism (6). Disengaged providers with high turnover intention may experience dissatisfaction with their job, burnout, and a loss of motivation to provide high-quality services. Furthermore, when providers voluntarily leave their job, their absence could have a negative effect at the agency level. For example, voluntary turnover may be more likely among higher-performing providers than lower-performing providers (7), potentially resulting in gaps in desirable clinical skill sets.
Although turnover intention and actual turnover are correlated, some studies suggest the two are influenced by distinct mechanisms. For example, the explained variance of actual turnover by intention is small to moderate (1), suggesting that differential factors may affect turnover intention compared with actual turnover. Therefore, it is imperative to understand what factors uniquely or commonly affect turnover intention and actual turnover.
No studies have examined the differential factors for turnover intention and actual voluntary turnover simultaneously among community mental health providers. In this study, we explored factors associated with both turnover intention and actual turnover among community mental health providers in an effort to develop better prediction models and ultimately to inform interventions that reduce high turnover among mental health providers.

Methods

This study used data originally collected from 195 direct care providers at two nonprofit community mental health centers in the Midwest for a burnout intervention trial (8). One rural site employed approximately 230 staff and served 6,000 clients annually, and an urban site employed 260 staff and served 4,000 clients annually. Both provided case management, home-based and school-based services, employment support, medication management, and outpatient individual and group services. The original study did not show any significant intervention effects on turnover outcomes. Participants were predominantly women (80%; N=149) and white (85%; N=158). Their mean±SD age was 40±12 years old, and their mean tenure at their agency was 5.1±6.3 years. Because we were interested in factors that affect voluntary turnover, we excluded involuntary termination (N=8) and missing (N=1) cases, resulting in a sample of 186 community mental health providers. Forty-eight participants (26%) voluntarily left their agency in the 12 months after the baseline survey.
Provider characteristics included gender (men versus women), race (white versus other), age, exempt employment status (exempt versus nonexempt), education (bachelor’s degree or lower versus master’s degree or higher), marital status (married versus not married), having children under age 5 (yes versus no), and years worked in the agency and field. Job stressors included emotional exhaustion—measured with the emotional exhaustion subscale of the Maslach Burnout Inventory—(Cronbach’s α=0.92) (9), work-life conflict (α=0.77) (10), overall job satisfaction (11), and overtime work, based on whether actual work hours exceeded those scheduled (yes versus no). Turnover intention was measured by averaging two items: how often participants had considered leaving in the past 6 months and how likely they were to leave in the next 6 months (1).
We obtained these measures from participants’ baseline surveys, then collected actual turnover data from their agencies 12 months later (coded as stayed versus turnover). We used correlations, chi-square tests, and independent-samples t tests to examine factors that were associated with turnover outcomes. The parent study was approved by the Indiana University-Purdue University Indianapolis Institutional Review Board.

Results

Participants who experienced higher emotional exhaustion, higher work-life conflict, and lower job satisfaction were more likely to indicate higher turnover intention at baseline (p<0.001) than those who did not (Table 1). Women reported higher turnover intention than did men (p=0.02). In contrast, none of those factors predicted actual turnover. Participants who voluntarily left their agency after 12 months were more likely to be younger (p=0.03), have children under age 5 (p=0.01), and have worked fewer years in the field (p=0.02) and at the agency (p=0.03). In addition, those who held a bachelor’s degree or lower (p=0.05) tended to leave their agency, and those who worked overtime tended to remain (p=0.08), although these results were not statistically significant. Exempt status, race, and marital status were not significant factors for either turnover intention or actual turnover. Turnover intention and actual turnover were significantly correlated with each other (p<0.001, Cohen’s d=0.6).
TABLE 1. Associations between turnover factors and turnover outcomes among 186 community mental health providersa
 Stayed (N=138)Turnover (N=48) Turnover intentionb  
Turnover factorN%N%pMSDrp
Provider characteristic         
 Age (mean±SD)41.4±12.0 37.0±11.7 .03  .00.98
 Work years in agency (mean±SD)5.6±6.8 3.6±4.4 .03  .09.24
 Work years in field (mean±SD)9.8±9.5 6.6±7.2 .02  –.01.90
 Gender    .85   .02
  Men2773.01027.0 1.97.87  
  Women11174.53825.5 2.421.46  
 Race    .99   .24
  White11774.04126.0 2.391.39  
  Other2074.1725.9 2.061.20  
 Education    .05   .95
  Bachelor’s or lower7568.83431.2 2.341.37  
  Master’s or higher6281.61418.4 2.321.39  
 Marital status    .44   .41
  Married6171.82428.2 2.411.40  
  Not married7676.82323.2 2.231.35  
 Children under age 5    .01   .47
  Yes1655.21344.8 2.321.45  
  No11477.63322.5 2.521.38  
 Exempt status    .12   .76
  Exempt3583.3716.7 2.391.32  
  Nonexempt10371.54128.5 2.321.39  
Job stressor         
 Emotional exhaustion (mean±SD)c2.36±1.2 2.72±1.6 .11  .65<.001
 Job satisfaction (mean±SD)d5.58±1.4 5.14±1.7 .14  –.67<.001
 Work-life conflict (mean±SD)e2.75±1.0 2.81±1.0 .70  .36<.001
 Overtime work    .08   .49
  No6368.52931.5 2.271.45  
  Yes7579.81920.2 2.401.29  
a
Analyses include correlations for both continuous variables, chi-square tests for both binary variables, and independent-samples t tests for the combination of binary and continuous variables.
b
Turnover intention was measured by averaging two items: how often participants had considered leaving in the past 6 months and how likely they were to leave in the next 6 months. Possible scores range from 1 to 6, with higher scores indicating higher turnover intention.
c
Measured with the emotional exhaustion subscale of the Maslach Burnout Inventory. Possible scores range from 0 to 6, with higher scores indicating more emotional exhaustion.
d
Measured with participants’ baseline surveys. Possible scores range from 1 to 7, with higher scores indicating higher job satisfaction.
e
Measured with participants’ baseline surveys. Possible scores range from 1 to 5, with higher scores indicating more work-life conflicts.

Discussion

This study is the first we are aware of that examined turnover factors for both turnover intention and actual turnover among community mental health providers. Turnover intention and actual turnover were correlated; however, the factors associated with each outcome were different. This finding is consistent with literature showing that factors that influence turnover do not always occur in a linear fashion (6). For example, turnover intention may trigger actual turnover, but personal circumstances (e.g., mobility, family situations) may be important factors as well.
Our findings suggest that turnover intention is influenced more by job stressors (increased emotional exhaustion and work-life conflict, and reduced job satisfaction) than by provider characteristics. Although this finding is consistent with previous research across an array of occupations (6), these stressors may be particularly relevant to mental health turnover outcomes (12). For example, mental health providers have identified time constraints and excessive workload as burdens (13). These challenges could increase job demands and create work-life conflicts, which may increase turnover intention. Furthermore, we found that women reported higher turnover intention than did men (but showed no difference in actual turnover). Conversely, one study outside of mental health found greater rates of actual turnover among women than men, but no difference in turnover intention (14). We are not aware of specific studies that examine gender differences in turnover intention and actual turnover in mental health settings in order to better understand this discrepancy. However, our sample was disproportionately composed of women (80%), which might have skewed the results. Additionally, a third variable could have linked gender to turnover outcomes. Given that women providers comprise a majority of the workforce in community mental health, further examination is needed to ascertain whether our finding can be replicated.
Aside from gender, provider characteristics may be more influential on actual turnover than on turnover intention. Those who are younger, at earlier career stages, and have lower levels of education may experience considerable need for professional growth and development and may be more open to outside job opportunities. Indeed, a previous study of substance abuse treatment providers found that the most cited reason for turnover included a new job or new opportunity (7). Those at earlier stages of career development may have more mobility than experienced workers when actually leaving their job. Furthermore, those who had children under age 5 tended to leave their agencies more often than those without young children. Increased likelihood of turnover may be related to domestic duties or financial needs associated with young children, but replication is needed. Finally, providers who worked more than scheduled tended to remain in their agencies. This trend was not initially expected, given that overtime work is correlated with increased job stress (15), which could lead to increased turnover. However, it is possible that those who make stronger commitments to their jobs exceed their work expectations. Further examinations are needed to understand more nuanced mechanisms in this relationship.
Several limitations should be acknowledged, given the preliminary nature of this study. First, this was a secondary data analysis, which limited the available variables for consideration. Other factors that may be relevant to turnover outcomes (e.g., salary, training, and promotion opportunities) could not be examined. Second, the generalizability is limited, given that the sample was originally recruited for a different research purpose. Third, the study’s aim was exploratory—to identify the factors that might be relevant to turnover intention and actual turnover among mental health providers. However, turnover is a complex phenomenon, involving interactive processes among different factors. We could not eliminate potential confounding factors nor address how these factors independently and interactively affect turnover outcomes. Understanding more-nuanced factors leading to a provider’s turnover decision requires detailed hypotheses in order to test specific associations and to link mechanisms to turnover outcomes. Reliable conclusions will require prediction models in future studies that can account for variable interactions, and qualitative studies can facilitate greater understanding of providers’ perspectives. Finally, studies with longer follow-up could help determine whether, and to what extent, turnover intention is an early stage of most actual turnover episodes, and whether turnover intention fluctuates over time.
Despite these limitations, this study has critical implications for practice and research in mental health workforce management. Although dissatisfied or emotionally exhausted providers may have increased thoughts, about leaving their job, the actual decision to leave may be influenced by provider characteristics or life circumstances, each requiring a different intervention. Mental health service agencies must critically attend to both providers’ job stressors and provider characteristics in order to retain providers with high morale who are committed to providing high-quality services.

Conclusions

Turnover research is emerging in mental health, yet examinations of factors related to turnover outcomes are still limited. Our exploratory study revealed that turnover intention was associated more strongly with job stressors (e.g., emotional exhaustion, work-life conflict, reduced job satisfaction) than with provider characteristics, and actual turnover was associated more strongly with provider characteristics (e.g., younger, at earlier career stage, having children under age 5) than with job stressors. Given that both turnover intention and actual turnover have important implications for both providers and agencies (1), it is imperative to consider factors influencing each outcome to better inform development of management practices that address high turnover in mental health. Further examination is warranted to better understand turnover factors and processes and, eventually, to build predictive and preventive models.

References

1.
Fukui S, Wu W, Salyers MP: Mediational paths from supervisor support to turnover intention and actual turnover among community mental health providers. Psychiatr Rehabil J 2019 (Epub April 4, 2019). doi:
2.
Bukach AM, Ejaz FK, Dawson N, et al: Turnover among community mental health workers in Ohio. Adm Policy Ment Health Ment Health Serv Res 2017; 44:115–122
3.
Cotton JL, Tuttle JM: Employee turnover: a meta-analysis and review with implications for research. Acad Manage Rev 1986; 11:55–70
4.
Griffeth RW, Hom PW, Gaertner S: A meta-analysis of antecedents and correlates of employee turnover: update, moderator tests, and research implications for the next millennium. J Manage 2000; 26:463–488
5.
Beidas RS, Marcus S, Wolk CB, et al: A prospective examination of clinician and supervisor turnover within the context of implementation of evidence-based practices in a publicly funded mental health system. Adm Policy Ment Health Ment Health Serv Res 2016; 43:640–649
6.
Holtom BC, Mitchell TR, Lee TW, et al: Turnover and retention research: a glance at the past, a closer review of the present, and a venture into the future. Acad Management Ann 2008; 2:231–274
7.
Eby LT, Burk H, Maher CP: How serious of a problem is staff turnover in substance abuse treatment? A longitudinal study of actual turnover. J Subst Abuse Treat 2010; 39:264–271
8.
Salyers MP, Garabrant JM, Luther L, et al: A comparative effectiveness trial to reduce burnout and improve quality of care. Adm Policy Ment Health Ment Health Serv Res 2019; 46:238–254
9.
Maslach C, Jackson SE, Leiter MP: MBI: Maslach Burnout Inventory, 4th ed. Menlo Park, CA, Mind Garden, 1996
10.
Carlson DS, Kacmar KM, Williams LJ: Construction and initial validation of a multidimensional measure of work-family conflict. J Vocat Behav 2000; 56:249–276
11.
Nagy MS: Using a single‐item approach to measure facet job satisfaction. J Occup Organ Psychol 2002; 75:77–86
12.
Scanlan JN, Still M: Job satisfaction, burnout and turnover intention in occupational therapists working in mental health. Aust Occup Ther J 2013; 60:310–318
13.
Salyers MP, Rollins AL, Kelly Y-F, et al: Job satisfaction and burnout among VA and community mental health workers. Adm Policy Ment Health Ment Health Serv Res 2013; 40:69–75
14.
Weisberg J, Kirschenbaum A: Gender and turnover: a reexamination of the impact of sex on intent and actual job changes. Hum Relat 1993; 46:987–1006
15.
Luther L, Gearhart T, Fukui S, et al: Working overtime in community mental health: associations with clinician burnout and perceived quality of care. Psychiatr Rehabil J 2017; 40:252–259

Information & Authors

Information

Published In

Go to Psychiatric Services
Go to Psychiatric Services
Psychiatric Services
Pages: 289 - 292
PubMed: 31658895

History

Received: 15 May 2019
Revision received: 31 July 2019
Accepted: 6 September 2019
Published online: 29 October 2019
Published in print: March 01, 2020

Keywords

  1. Community mental health provider
  2. Turnover intention
  3. Actual turnover
  4. Job stress
  5. Burnout
  6. Job satisfaction

Authors

Details

Sadaaki Fukui, Ph.D., M.S.W. [email protected]
Indiana University School of Social Work, Indianapolis (Fukui); U.S. Department of Veterans Affairs (VA) Health Services Research and Development Center for Health Information and Communication, Roudebush VA Medical Center, and Regenstrief Institute, Indianapolis (Rollins); Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis (Rollins, Salyers).
Angela L. Rollins, Ph.D.
Indiana University School of Social Work, Indianapolis (Fukui); U.S. Department of Veterans Affairs (VA) Health Services Research and Development Center for Health Information and Communication, Roudebush VA Medical Center, and Regenstrief Institute, Indianapolis (Rollins); Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis (Rollins, Salyers).
Michelle P. Salyers, Ph.D.
Indiana University School of Social Work, Indianapolis (Fukui); U.S. Department of Veterans Affairs (VA) Health Services Research and Development Center for Health Information and Communication, Roudebush VA Medical Center, and Regenstrief Institute, Indianapolis (Rollins); Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis (Rollins, Salyers).

Notes

Send correspondence to Dr. Fukui ([email protected]).

Competing Interests

The authors report no financial relationships with commercial interests.

Funding Information

The study was supported by grant IH-1304-6597, awarded by Patient-Centered Outcomes Research Institute.

Metrics & Citations

Metrics

Citations

Export Citations

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

For more information or tips please see 'Downloading to a citation manager' in the Help menu.

Format
Citation style
Style
Copy to clipboard

View Options

View options

PDF/EPUB

View PDF/EPUB

Get Access

Login options

Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.

Personal login Institutional Login Open Athens login
Purchase Options

Purchase this article to access the full text.

PPV Articles - Psychiatric Services

PPV Articles - Psychiatric Services

Not a subscriber?

Subscribe Now / Learn More

PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

Media

Figures

Other

Tables

Share

Share

Share article link

Share