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Health Serv Res. 2008 August; 43(4): 1403–1423.
PMCID: PMC2517268
NIHMSID: NIHMS44297

Predicting Changes in Staff Morale and Burnout at Community Health Centers Participating in the Health Disparities Collaboratives

Abstract

Objective

To identify predictors of changes in staff morale and burnout associated with participation in a quality improvement (QI) initiative at community health centers (HCs).

Data Sources

Surveys of staff at 145 HCs participating in the Health Disparities Collaboratives (HDC) program in 2004.

Data Collection and Study Design

Self-administered questionnaire data collected from 622 HC staff (68 percent response rate) were analyzed to identify predictors of reported change in staff morale and burnout. Predictive categories included outcomes of the QI initiative, levels of HDC integration, institutional support, the use of incentives, and demographic characteristics of respondents and centers.

Principal Findings

Perceived improvements in staff morale and reduced likelihood of staff burnout were associated with receiving personal recognition, career promotion, and skill development opportunities. Similar outcomes were associated with sufficient funding and personnel, fair distribution of work, effective training of new hires, and consistent provider participation.

Conclusions

Having sufficient personnel available to administer the HDC was found to be the strongest predictor of team member satisfaction. However, a number of low-cost, reasonably modifiable, organizational and leadership characteristics were also identified, which may facilitate improvements in staff morale and reduce the likelihood of staff burnout at HCs participating in the HDC.

Keywords: Employee satisfaction, community health centers, quality improvement

The introduction of quality improvement (QI) initiatives can present special challenges for health care settings that have limited personnel and financial resources, and high attrition among key staff. In the context of such constraints, the difficulty of balancing daily clinical demands with additional QI responsibilities can lead to lowered staff morale, burnout, and additional turnover, all of which can further compromise quality of care (Cavanagh 1989; Goodall 1993; Busteed, Barwick, and Grubb 1994). While the concerns regarding lowered staff morale are significant, other studies with contradictory findings show that the introduction of QI initiatives can actually lead to improved job satisfaction (Counte et al. 1992; Calomeni, Solberg, and Conn 1999; St. Pierre 2006) by introducing new employee incentives, such as opportunities for training and professional development (Akerboom and Maes 2006; Gilbody et al. 2006).

Institutional and physician support can affect both staff satisfaction and the overall success of the QI effort (Neuhauser 2002; Gollop et al. 2004), and there is further evidence that organizational factors such as culture, group cohesion, and support can limit the detrimental effects of stressful work demands on staff in acute inpatient care and long-term care settings (Karsh, Booske, and Sainfort 2005; Shermont and Krepcio 2006). However, relatively little is known about the impact of QI initiatives in resource-challenged ambulatory care settings, and the relative influence of these ancillary factors associated with its implementation, including the use of incentives, levels of QI integration, and institutional support.

The Health Resources and Services Administration's Bureau of Primary Health Care (BPHC) funds and oversees over 1,000 community health centers (HCs) that provide care for approximately 15 million medically underserved people at 5,000 sites (National Association of Community Health Centers 2006). The mission of the HCs is to serve as a safety net for individuals who face major barriers to accessing quality health care, including the medically isolated, uninsured, and vulnerable (Sardell 1988). In keeping with its commitment to quality health care and in an attempt to improve health care for all while eliminating disparities, in 1998 the BPHC began the Health Disparities Collaboratives (HDC) on a national basis. The HDC is a sweeping QI initiative that uses the MacColl Institute's Chronic Care Model and the Model for Improvement developed by Associates in Learning (Langley et al. 1996; Wagner, Austin, and Von Korff 1996). The HDC involve HCs in regional or national learning sessions conducted on a quarterly basis during the first year of implementation. Initially 88 HCs participated in a collaborative that targeted improvements in diabetes care and outcomes. Subsequent HDCs have focused on asthma, depression, cardiovascular disease, cancer screening and planned care, finance, and clinical systems redesign. As of April 2007, 1,009 HCs were participating in the HDC (Charles Daly, Health Resources and Services Administration, April 19, 2007, personal communication).

Participation in the HDC requires commitment of resources to attend learning sessions, establish and maintain information systems, and introduce and monitor the impact of systems changes. The HDC have been clearly found to improve processes of chronic care management for multiple conditions (Chin et al. 2007; Landon et al. 2007). Patient outcomes such as glucose control levels in diabetes have been found to improve after long-term implementation of the HDC (4-years) (Chin et al. 2007). While the primary aim of HDC activities is to improve patient outcomes (BPHC 2006), their introduction may also produce additional challenges for HCs struggling with already strained resources. For example, an evaluation of the first year of the HDC (i.e., 1999) found that, although quality of diabetes care improved and the HDC was generally well-received by staff, time burdens associated with collaborative activities and staff turnover were prevalent (Chin et al. 2004), although it is unclear if such burden persists once HDC requirements have been integrated into HC practices. Similarly, in an evaluation of 153 HCs participating in the HDC for at least 1 year as of 2003, collaborative team members reported spending on average between 7.6 and 10.7 hours/week working strictly on HDC activities. Among these HCs, fully 21 percent reported having no one with paid protected time for leading QI activities, and a significant proportion of the HC staff surveyed reported that lack of resources and time were significant barriers to implementing the HDC. Moreover, 33 percent reported concerns of staff burnout and 18 percent reported decreased staff morale (Chin et al. in review).

These findings raise critical questions about the causes of declining morale and increasing burnout, as well as whether HC leadership can take steps to mitigate these effects. Factors that influence staff morale and the risk of burnout can be either modifiable or nonmodifiable, and can operate at the level of the individual employee or at the level of the HC's structure and resources. By identifying such relationships, HCs implementing the HDC can be better prepared to anticipate and intervene to reduce the risk for staff burnout, lowered staff morale, and by extension, increased staff attrition. This paper seeks to identify predictors of variation in staff morale and burnout in the current HC setting.

METHODS

Researchers at the National Opinion Research Center (NORC), the University of Chicago, and the MidWest Clinicians' Network surveyed HC staff to measure possible effects and consequences resulting from implementation of the HDC. The development of survey instruments was a collaborative process and included researchers directly involved with HDC implementation efforts. Our design efforts were further informed by the results of qualitative, structured interviews previously conducted with 40 staff members at eight HCs. This range of input was necessary as we anticipated wide variation among HC staff with regards to knowledge of and participation in the HDC. In the end, we developed five versions of the survey instrument, each tailored to the respondent's specific role at the HC and in the collaborative effort. The majority of items in the instruments were common across respondent types; common questions were identical in wording, response options, and format. However, some respondent types received additional questions not asked of others. Most survey items included a five-point Likert response scale, and addressed attitudes regarding the QI effort including: perceptions of clinical outcomes and costs; support from staff and leadership; resource availability; and employee satisfaction.

The sampling frame was restricted to centers located in the Midwest and West Central regions of the United States, which had participated in the HDC for at least 1 year. While there are several conditions that are officially covered by the HDC, our sampling criteria were not based on any official or unofficial expansion of the protocols to other diseases. Of the 173 HCs identified, 95 percent provided complete staff listings including individual names, positions at the center, and role, if any, in the HDC. Our sample was then selected from this list, and included all identified Chief Executive Officers, Executive Directors, Medical Directors, HDC Team Leaders, HDC Team Members, and a random sample of up to three staff members not participating in the collaborative effort. A respondent's position at the HC had no effect on his or her eligibility, and our sample was not restricted to practitioners.

Data were collected between March and December 2004 following the standards of Dillman's Total Design Method (Dillman 1978). Our initial mailing was sent to more than 1,500 eligible respondents, and up to two additional copies of the questionnaire were sent to nonrespondents via express delivery. To further increase response we conducted telephone prompting and mailed additional surveys along with letters of support by relevant BPHC officials. These efforts produced a final overall response rate of 68 percent, with questionnaire-specific rates ranging from 79 percent for Team Leaders, 71 percent for Team Members, to 58 percent for staff not participating in the Collaborative.

Outcome Measures and Covariates

To examine the impact of the HDC on reported job satisfaction we analyzed data from two of the five surveys described earlier—Team Leader and Team Member—the two respondent types most heavily involved in the HDC effort. Two measures of job satisfaction are addressed: staff morale and team member burnout. The concept of employee burnout has evolved over time but is generally defined by an employee's feelings of discouragement and dissatisfaction and a decline in effectiveness in their work (Maslach, Schaufeli, and Leiter 2001).

All respondents were asked the following questions regarding morale and burnout. Perceived change in staff morale was measured by a single survey item that asked, “During calendar year 2003, to what degree did the Collaborative worsen or improve staff morale at your center?” Response options were “greatly worsened,”“somewhat worsened,”“no change,”“somewhat improved,” and “greatly improved,” with higher scores reflecting perceptions of improved staff morale. Perceived staff burnout was measured by a single survey item that asked respondents to rate their degree of agreement with the statement, “Team members became burned out because of their Collaborative duties.” Response format was a five-point Likert-type scale with options ranging from “Strongly Disagree” to “Strongly Agree,” with higher scores reflecting greater agreement with the statement.

Predictive variables are grouped into distinct categories: HDC quality of care outcomes; HDC integration; institutional support of the HDC; the use of incentives; and demographic characteristics—with categories including both modifiable and nonmodifiable characteristics of respondents and HCs. Each category is described below and specific questionnaire items used as independent variables and covariates in our models are presented in Table 1.

Table 1
Questionnaire Items Used as Independent Variables and Covariates with Respective Response Options

HDC quality of care outcomes are measured by two separate indicators. These measures are not used to evaluate whether actual quality of care increased as a result of the HDC effort, but rather are used as surrogates of overall HDC performance in our analysis of the predictors of staff satisfaction. The first measure is a composite summary score of five items measuring respondents' self-reported perceptions of overall success of the HDC effort, perceived value of the collaborative effort, and perceived improvements to processes and patient outcomes and satisfaction. Each measure included a five-point Likert response scale; the sum of individual measures gave us a total score from 5 to 25. The resulting summary score was then rescaled, based on its distribution, to create a final three-point scale ranging from “little or no improvement” to “great improvement.” Complete descriptions of each component measure are provided in Table 1.

For a second, independent assessment of outcomes, eight regional HDC Cluster Coordinators were asked to evaluate the success of each center with which they worked (Cluster Coordinators work with multiple HCs to provide technical assistance through telephone calls, information management, access to key materials required for the HDC effort, and regional meetings). For each HC the following question was asked: “For 2004, how would you rate the overall performance and participation of each of the following centers in the HDC” with the response categories of “Excellent,”“Very Good,”“Good,”“Fair,” and “Poor.” In making these assessments Cluster Coordinators had access to self-reported performance measures from each HC, but it is unknown to what extent this information was used during assessments, and these data were not available to us for supplemental analyses.

HDC Integration measures the degree to which collaborative activities are embedded within centers, including staff participation levels. Institutional support, in contrast, addresses allocation of resources by HCs, while incentives refer to whether HDC team members receive monetary or nonmonetary benefits for their participation.

We further divided predictive variables into those that were modifiable and nonmodifiable by HDC policy changes. There are no established definitions of modifiable and nonmodifiable variables facilitating QI programs. We defined a nonmodifiable variable as any respondent or HC characteristic that was fixed for a respondent at the time the survey was completed (i.e., length of tenure at the HDC, rural location) and not affected by changes in HDC policies. In comparison, modifiable characteristics were those that can be influenced by policy change, such as the provision of incentives for QI activities or opportunities for professional development of new skills related to the HDC. These definitions and assignments were determined by consensus of all investigators but we recognize that specific characterizations could be debated.

To better ensure accurate and consistent measurement of HC-level, nonmodifiable descriptors, we also included key measures from the Uniform Data System (UDS), an annual compilation of demographic information on BPHC-funded HCs and their patient populations. Specifically, UDS data were used to identify (1) the percent of patients that are primarily non-English speaking; (2) the percent of HC patients with private health insurance coverage; (3) the total number of medical staff (FTEs) at each HC; and (4) the total number of medical visits per FTE. UDS data are from 2004 and are reported at the HC-level with records for HCs with multiple sites describing characteristics of all associated sites combined, while survey data are reported for each specific site individually.

Analysis

Descriptive statistics were used to characterize respondents and HCs. Associations of job satisfaction indices with modifiable and nonmodifiable characteristics of HCs and individuals were evaluated using mixed linear regression models (Drum 2002; SAS Institute Inc. 2004). Mixed models are characterized by the inclusion of both fixed and random factors; in this case, the fixed factors are the covariates of interest, and the single random factor is the HC. Our purpose in specifying HC as a random factor was to incorporate variation due to clustering of respondents within HCs in order to obtain appropriate standard errors for regression coefficients.

Bivariate associations were examined first. Next, for each dependent variable, four preliminary models were developed (not shown)—each including measures that intersected by modifiability and unit of analysis (i.e., Model 1: Nonmodifiable HC variables [e.g., HC located in a rural area]; Model 2: Modifiable HC variables [e.g., provider participation in the HDC decreased]; Model 3: Nonmodifiable team member variables [e.g., gender of respondent]; Model 4: Modifiable team member variables [e.g., received extra money for work on HDC]). Finally, separate multivariate models were fit for each dependent variable, which included all covariate domains. Covariates that were significant in the preliminary models were candidates for inclusion, and stepwise procedures were used to obtain the most parsimonious model (Searle, Casella, and McCulloch 1992; Verbeke et al. 1997). This multi-model approach was completed twice for each dependent variable, once controlling for HDC outcomes in order to measure the direct effects of the covariates above and beyond their effects through success of the HDC, and once ignoring HDC outcomes to estimate the combined direct and indirect effects of the covariates. Below we present results from these final, most parsimonious models.

Distributions of dependent variables were examined before analysis to assess the suitability of a linear model. Mixed linear regression models were fit using the PROC MIXED procedure, SAS version 9.1.

RESULTS

Characteristics of Respondents and HCs

Our sample consisted of a total of 504 Team Members and 118 Team Leaders representing 145 HCs. The mean number of responding team members was 4.3 per center, and the mode was 3.0 per center. More than 80 percent of respondents were females, 9 percent were physicians, and 36 percent were nurses. Forty percent were responsible for a panel of patients. A broad range of other clinical care providers were also represented including health educators, social workers, and dieticians.

Forty-four percent of respondents reported working at a rural HC. Overall, the sampled HCs served a patient population that was, on average, 19 percent primarily non-English speaking, with an average of only 15 percent of patients who were covered by private health insurance. Fully 82 percent of the primary care providers at the respondents' HCs participated in the HDC. Staff turnover during the past year of key positions—Team Leader and Medical Director—was reported by 18 and 16 percent of respondents, respectively.

On average, respondents had worked at the HC for approximately 6 years and had been in their HDC role for 2 years. Respondents reported working approximately 40 hours/week, and spending eight of those hours on HDC activities. When asked how much personal recognition, career promotion, or skills development they received for their role in the HDC, respondents reported, on average, “only a little bit.” Fewer than 20 percent received any release time for their HDC work, and only 3 percent reported receiving monetary incentives.

Staff Morale and Burnout

Approximately 20 percent of respondents reported staff morale “somewhat worsened” or “greatly worsened” as a result of the HDC, while 40 percent reported that staff morale “somewhat improved” or “greatly improved.” More than 30 percent agreed or strongly agreed that “team members became burned out because of their collaborative duties,” while 30 percent disagreed or strongly disagreed with that statement. Further, one-third of respondents reported that their HC had not allocated any paid time for at least one of three key HDC activities (data entry, registry maintenance, and QI interventions), and fewer than half agreed that there was sufficient funding or personnel to run the HDC, that HC personnel shared the HDC workload fairly, and that new hires were effectively trained in the collaborative model.

Predicting Changes in Staff Morale and Burnout

Table 2 provides univariate descriptive statistics for individual and HC-level predictors of staff morale and burnout, both nonmodifiable and modifiable, and all associated univariate regression coefficients for each dependent variable. Perceived improvements in staff morale and reduced likelihood of staff burnout were significantly associated with receiving personal recognition, career promotion opportunities, and skill development opportunities from HC leadership. They were also significantly associated with reports of positive quality of care outcomes, and sufficient funding and personnel. Finally, improvements in morale and reduced burnout were correlated with reports that the HDC workload was shared fairly, new staff were trained effectively, and providers did not decrease their participation.

Table 2
Characteristics of Respondents and Health Centers with Univariate Regression Coefficients Predicting Change in Morale and Burnout among HDC Team Members (n = 622)

The final mixed linear regression models, presented in Table 3, illustrate changes in staff morale and burnout. Two models of morale are presented. The first, not controlling for any effects of HDC outcomes, describes both the direct and indirect effects of each covariate on morale. The second model does control for self-rated HDC outcomes and therefore describes only the direct effects of the covariates. Two equivalent models measuring staff burnout were developed but found no difference in effects whether or not we controlled for HDC outcomes. As a result only the model of burnout that does not control for HDC outcomes is presented. It should be noted that the HDC outcome determination made by Cluster Coordinator assessment was not a significant predictor for either measure of staff satisfaction and therefore was not included in our models.

Table 3
Predictors of Staff Morale and Burnout Associated with the Health Disparities Collaboratives*

Morale

Our initial model to predict changes in staff morale—not controlling for any effects of HDC outcomes—identified seven significant predictor variables. Covariates found to predict increases in morale include: sufficient staffing for the HDC; effective training of new hires in the HDC model; leadership support; fair distribution of HDC tasks; career promotion opportunities; and the presence of a strong champion of the HDC. Decreases in provider participation were found to be significantly associated with decreased morale.

However, once we introduced HDC outcomes as covariates in our second model, the impact of two of these seven measures was eliminated: effectively training new hires in the HDC model; and whether a strong champion of the HDC was present at the HC. The remaining predictors of morale were unaffected by the presence of HDC outcomes and the self-rated measure of HDC outcomes was itself found to be a significant predictor. In total, this second model of morale produced significant associations for six independent variables, with the strongest associations found for respondents' positive perceptions of HDC outcomes, and sufficient HDC staffing levels.

Burnout

When examining factors related to staff burnout, we found that sufficient staffing levels and the fair distribution of HDC tasks were the most strongly associated predictors of reduced burnout, findings that echo their earlier noted positive effect on increased morale. Decreased levels of team member burnout were also related to receipt of opportunities for skills development. In contrast, our models identified two covariates associated with increases in staff burnout: employment at the rural HC; and longer tenure in one's role on the HDC.

DISCUSSION

In health care systems with limited resources, the challenge of balancing daily clinical demands with additional QI responsibilities may lead to unexpected and often contradictory outcomes. New opportunities for professional development and measurable improvement in patient outcomes can serve as strong personal motivators for HDC team members. However, there may be unforeseen challenges when implementing HDC requirements which could ultimately lead to lowered staff satisfaction and possibly increased attrition. Using survey data, we examined reported staff morale and staff burnout associated with HC staff participation in the HDC, with the goal of identifying factors at the organizational and individual levels that could be modified to facilitate improvements in staff morale and reduced the likelihood of staff burnout. While some results within four key categories (HDC outcomes, HDC integration, institutional support, incentives) may imply that additional monetary resources are needed to buffer against employee dissatisfaction, the vast majority of findings suggest a number of organizational and leadership factors that are reasonably modifiable, implemented at little cost, and could facilitate improvements in staff morale and reduce the likelihood of staff burnout at HCs.

HDC Outcomes

We found higher estimates of increased staff morale among respondents who held positive opinions of the HDC in general. However, this association was true when measuring respondent's perceptions only, not the more objective evaluation offered by the Cluster Coordinator. This finding is somewhat expected as we would anticipate higher correlations between measures collected from the same respondents than those from an external source (i.e., Cluster Coordinators). In addition, self-reported evaluations by Team Members and Team Leaders are likely to be more positive given their significant investment in the HDC. We expect this effect to be self-reinforcing and multi-directional, with morale increasing among those who believe the HDC effort was successful, and higher levels of morale providing motivation to make the HDC successful. Neither outcome measure was found to be significantly associated with levels of burnout.

HDC Integration

A key focus of our research was to determine how well the HDC has been integrated into HCs and to identify issues concerning “buy in” from providers and center leadership. Not surprisingly, we found significant associations between employee satisfaction and three measures of collaborative integration—two of which can be modified to increase levels of staff morale. Specifically, we found that decreases in HDC participation among providers had damaging effects on morale, while the presence of a strong champion of the collaborative effort increased morale among team members. However, this latter association did not remain significant when controlling for the effect of overall HDC outcomes. This finding emphasizes the power of team members' own perceptions of HDC outcomes. On the other hand, because a positive, indirect relationship was found between increased morale and the presence of a strong champion, it would suggest that strong leadership for the HDC effort is useful and may contribute to the positive correlation between self-perceptions of successful outcomes and high staff morale described above.

Institutional Support

Our results identify three unique measures of institutional support that are significantly associated with increased staff morale or decreased levels of burnout. While the strongest predictor was sufficient levels of HDC staff, the other three measures represent organizational adjustments that require little or no addition of resources, including the fair distribution of HDC tasks, and the support of HC leadership.

Incentives

While we found that employee incentives are effective in maintaining satisfaction, few team members actually received direct monetary rewards. Instead, team members responded to benefits received as a result of their HDC work—including skills development and career promotion. These results could be used by HC leadership to structure tasks so that the direct benefit to employees is emphasized.

One limitation to the present study is our reliance on a self-report survey without objective measures of staff morale or staff burnout. However, there are no objective gold standard measures of these outcomes, and our survey measure did capture respondents' perceptions of job satisfaction at their respective HCs, which they attributed to their HCs' participation in the HDC. Our data also measured how supportive team members perceived HC leadership to be and the value given to offered incentives. Nevertheless, to better understand the impact of the HDC on staff satisfaction and burnout, additional research that explicitly measures levels of morale, burnout, and staff attrition in more detail is needed. Similarly, future research that measures other types of institutional support would be useful, such as the impact of regional and national Cluster Coordinators provided by BPHC. Such research should also seek to determine the impact of locality on these measures, as concerns about HC staff attrition in rural areas is already well established (Singer et al., 1998; Rosenblatt and Hart 1999; Rosenblatt et al., 2006) and was found to be a significant predictor of burnout in this research.

Another important limitation is the lack of objective performance indicators describing HDC outcomes. Future research would benefit from these data to not only evaluate the impact of the HDC but also to better understand the relationship of HC performance to other key covariates such as HDC integration and institutional support. Lastly, caution should be used when extending these findings to HCs that are not funded by BPHC and have not received the training and resources available through participation in the HDC.

In sum, this study suggests critical steps that can be taken by HC leadership to preserve or bolster job satisfaction among employees involved in the HDC, many of which could be implemented at little or no cost. We found morale to be significantly related to perceived support of HC leaders, with provider resistance to the HDC associated with decreased morale. Further, key nonmonetary management tools, including fair distribution of HDC tasks, were associated with both improved morale and decreased burnout, and we suggest that HC leadership consider such options as a way to maintain staff satisfaction when implementing the HDC or other organizational changes.

Acknowledgments

Grant Support: This project was supported by Grant Number 1 U01 HS13635 from the Agency for Healthcare Research and Quality, with support from the Health Resources and Services Administration. Additional support came from the Agency for Healthcare Research and Quality (R01 HS10479), and the National Institute of Diabetes and Digestive and Kidney Diseases Diabetes Research and Training Center (P60 DK20595). Dr. Chin is supported by a Midcareer Investigator Award in Patient-Oriented Research from the National Institute of Diabetes and Digestive and Kidney Diseases (K24 DK071933) and was a Robert Wood Johnson Foundation Generalist Physician Faculty Scholar. Dr. Huang is supported by a Career Development Award from the National Institute on Aging (K23 AG021963). Dr. Heuer was a Robert Wood Johnson Executive Nurse Fellow.

Disclosures: There are no financial or other conflicts to report.

Disclaimers: There are no disclaimers to report.

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