The literature includes studies that consider related, but different dependent variables (i.e., turnover intention versus actual turnover). Retrospective data may be more prone to hindsight biases than data collected prospectively. For instance, studies often rely on participants’ retrospective reports of factors that may have contributed to their decision to leave. While there is increased interest in assessing community behavioral health professionals’ turnover rates, the existing studies have limitations. While some studies show that turnover is predicted by high salary ( Tang, Kim, & Tang, 2000), there is mounting evidence that low salary is associated with greater turnover ( Ben-Dror, 1994 Beidas et al., 2015 Bukach et al., 2015 Sheidow, Schoenwald, Wagner, Allred, & Burns, 2007). Many studies found that negative organizational cultures (shared beliefs and expectations about day-to-day functions) and climates (staff perceptions of the work environment) are associated with higher turnover rates ( Aarons & Sawitzky, 2006 Aarons et al., 2011 Beidas et al., 2015 Glisson, Schoenwald, Kelleher, Landsverk, Hoagwood, Mayberg, & Green, 2008).ĭata are mixed about the impact of salaries on behavioral health workforce turnover rates. One state-wide survey of community behavioral health agencies found that larger agencies had higher turnover rates ( Bukach et al., 2015). Recent studies examined organizational variables associated with turnover. But many characteristics are unique to behavioral health staff (e.g., emotional nature of the work, educational requirements, pay rates) which might limit generalization of these findings.Įarly studies of the behavioral health workforce focused on individual characteristics and found that high turnover rates were associated with younger age, having a master’s degree (rather than more or less education) ( Ben-Dror, 1994 Blankertz & Robinson, 1997), and high burnout and job stress ( Aarons, Sommerfeld, Willging, Sommerfield, & Wilging, Cathleen, 2011 Beidas, Marcus, Wolk, Powell, Aarons, Evans…Mandell, 2015). In the general workforce literature, characteristics associated with higher rates of turnover include having a greater number of children, shorter tenure with the agency, weak organizational commitment, poor leadership, low perceived autonomy, and low job satisfaction ( Griffeth, Hom, & Gaertner, 2000). To identify options for reducing these rates, several studies examined factors associated with turnover. Even the most conservative estimate (30%) is three times the 10% annual turnover considered ideal ( Smith & Rutigliano, 2002). Thus, turnover is not always negative ( Iglehart, 1990) because it can result in hiring stronger performing employees ( Wieder & Kruszynski, 2007).ĭespite the potential benefits of turnover (e.g., opportunities to hire stronger employees), annual rates in the behavioral health field far surpass what is considered a “healthy” turnover rate ( Hoge, Stuart, Morris, Flaherty, Manuel & Goplerud, 2013 The Annapolis Coalition, n.d.). Further complicating this issue is the complexity in understanding and measuring turnover given that some employees are asked to leave, and some underperforming employees choose to leave. Turnover affects virtually every aspect of community-relevant studies (e.g., family recruitment, practitioner recruitment, training, treatment implementation), which inhibits studies’ viability (e.g., Brabson, Harris, Lindhiem & Herschell, under review). Not only does turnover result in service and relationship disruption for consumers, there also are substantial costs associated with recruitment, training, and supporting new staff ( Ben-Dror, 1994 Bjorklund et al., 2009), as well as strain on remaining staff when others leave ( Iglehart, 1990). High rates of staff turnover impact service delivery. Turnover rates within the behavioral health and substance abuse workforce are estimated to be 30% to 50% each year ( Bjorklund, Monroe-DeVita, Reed, Toulon, & Morse, 2009 Bukach, Ejaz, Dawson, & Gitter, 2015 Garner, Hunter, Modisette, Ihnes, & Godley, 2012 Substance Abuse and Mental Health Services Administration, 2013 Zhu, Wholey, Cain, & Natafgi, 2016), with some estimates reaching 70% ( Ben-Dror, 1994 Selden, 2010). However, the viability of the current community behavioral health workforce is questionable. These efforts depend on a workforce that can learn, implement, and sustain EBPs. Clinical initiatives have trained community practitioners in EBPs at federal, state, and local levels. Effectiveness trials have tested the utility of specific treatments. Implementation of evidence-based practices (EBPs) to treat behavioral health conditions in community settings has been identified as a national priority (e.g., National Institute of Mental Health, 1998 New Freedom Commission, 2003).
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