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BurnoutandPhysicalHealthamongSocialWorkers-AThree-YearLongitudinalStudy.pdf

Burnout and Physical Health among Social Workers: A Three-Year Longitudinal Study

Hansung Kim, Juyeji, and Dennis Kao

The high risk of burnout in the social work profession is well established, but little is known about burnout's impact on the physical health of social workers. This article examines the relationship between burnout and physical health, using data from a longitudinal study of social workers. California-registered social workers (N = 406) were surveyed annually over a three-year period. Using structural equation modeling, the authors conducted a path analysis to test whether burnout predicted changes in physical health over time. The results showed that social workers with higher initial levels of burnout later reported more physical health complaints. Moreover, higher levels of burnout led to a faster rate of deterioration in physical health over a one-year period.The potential implications for policy and social service organizations are discussed.

KEYWORDS: bumout; longitudinal data; physical health; social workers

Social workers deliver a broad range of vital services and, thus, play a critical role in en- suring the health and well-being of society's

most vulnerable members. Ho'wever, the high demand for their services—coupled with increas- ingly diminishing resources—can present significant challenges for social workers. According to a recent national study (Whitaker,Weismiller, & Clark, 2006), social workers face increasing levels of paperwork and inadequate supervision. Beset by chronic staff shortages and turnover, social workers tend to be overworked and are often asked to take on large cli- ent caseloads. Given the heavy demands placed on them, it is not surprising that social workers often experience psychological distress and, eventually, high levels of burnout.

Burnout is a prolonged psychological response to chronic workplace Stressors and is theorized to include three dimensions: emotional exhaustion, depersonalization or cynicism, and diminished per- sonal accomplishment (Maslach, Schaufeli, & Leiter, 2001). Over the past decade, clear empirical evidence has emerged regarding the prevalence of burnout in the social work profession. For example, on the basis of a sample of 751 social workers, Siebert (2005) found that about three-fourths reported having had trouble with burnout during their careers. This high prevalence of burnout among social workers has been found across the broad range of practice fields. For example, Oktay (1992) found high lev-

els of emotional exhaustion and depersonalization among hospital AIDS social workers. Poulin and Walter (1993a) examined burnout among 1,196 gerontological social -workers and found that about 60 percent of them were experiencing moderate to high levels of emotional exhaustion. Similarly, Lloyd and King (2004) reported that Australian social workers working in mental health settings exhibited high levels of emotional exhaustion. In a study of 151 fiontline child protective services (CPS) workers, Anderson (2000) found that about 62 percent of participants were experiencing high levels of emotional exhaustion.

Social worker burnout is a serious problem be- cause it can adversely affect the quality and stability of social services. The hterature suggests that social workers experiencing burnout have an increased risk of psychological distress, such as depression (Evans et al., 2006; Siebert, 2004; N. Stanley, Manthorpe, & White,2007), which can ultimately lead to increased turnover (Kim & Stoner, 2008; Mor Barak, Nissly, & Levin, 2001). Although a significant number of research studies have shown the negative effects of burnout on social workers'practice and performance outcomes, less is understood about the effects of burnout on the physical health of social workers.

The role of psychological distress in physical illness is more established for the general workforce. For example, in a 20-year follow-up study based on a U.S. national sample, Ferraro and Nuriddin (2006)

258 ccc Code: oc;" ::ir..;_"_"" _"' I Workers

found that high levels of distress raised mortality risk. Darr and Johns (2008) recently conducted a meta-analysis to examine the negative effects of work strain on the psychological and physical symptoms that often lead to absenteeism at work; their results suggest that psychological symptoms do affect physical illness. Extensive research has shown that job-related Stressors and strain can have adverse health effects through several potential mechanisms, such as decreased immunity functioning and poor health behaviors (Melamed, Shirom,Toker, Berliner, & Shapira, 2006). For example, Nakamura, Hágase, Yoshida, and Ogino (1999) found that deperson- alization (one of the dimensions of burnout) was associated •with diminished cellular immunity. Other studies suggest that job Stressors can also lead to negative health behaviors, such as smoking, alcohol or substance abuse, and less exercise (Johansson, Johnson, & Hall, 1991)—all activities known to have adverse effects on a person's health (F. Jones & Bright, 2001).

In the burnout literature, there is substantial evidence on how burnout can affect the physical health of a country's workforce. For example, in a national study of workers in Finland, Honkonen et al. (2006) found that physical illness was more common among workers with burnout, includ- ing musculoskeletal disorders among women and cardiovascular diseases among men. In sum, the research has shown that burnout can negatively affect overall self-rated health status (Peterson et al., 2008) and can lead to a broad range of health problems, including somatic complaints (Soares, Grossi, of Sundin, 2007); cardiovascular diseases (Melamed et al., 2006; Toker, Shirom, Shapira, Ber- liner, & Melamed, 2005); sleep disturbances (Grossi, Perski, Evengard, Blomkvist, & Orth-Gomer, 2003; Söderström, Ekstedt, Âkerstedt, Nilsson, & Axelsson, 2004); headaches (T. L. Stanley, 2004); flu-hke illnesses, common colds, or incidences of gastroenteritis (Mohren et al, 2003); and illness- related absences from work (Burke & Mikkelsen, 2006). Furthermore, job stress—which can cause burnout—is a significant predictor of gastrointes- tinal problems (EdéU-Gustafson, Kritz, & Bogren, 2002; Sveinsdóttir, Gunnarsdóttir, & Riksdóttir, 2007). Diminishing physical health can lead to lost workdays, diminished job effectiveness, permanent disabilities, and increased compensation for sick leave (Eriksen, Svendsrod, Ursin, & Ursin, 1998; Schwartz, Stewart, Simon, & Lipton, 1998).

The relationship between burnout and physical illness has also been found among professionals who may work in settings similar to those of social workers. Among Swedish health care •workers, for example, Peterson et al. (2008) found that burnout was associated vî ith lower self-rated health status, sleep disturbance, and neck and back pain.

Despite the high risk of burnout in the social work profession, little is known about its impact on the physical health of social workers.To address this gap in the research, the present study used longitu- dinal data from a sample of social workers registered in California to examine the impact of burnout on their physical health.

METHOD

Data and Sample Data were drawn from a longitudinal study examin- ing the job-related factors associated •with burnout among social workers. In this study, participants •were randomly selected from a California registry of clinical social workers and surveyed three times (ap- proximately annually) for a three-year period from 2005 to 2007. Information was gathered through the use of mail questionnaires, which included both standardized instruments (for example, the Maslach Burnout Inventory—Human Service Survey [Maslach & Jackson, 1986]) and sociodemographic measures. The physical health measures were not included in the baseline survey (in 2005), but they were used in the second and third surveys (in 2006 and 2007) .When resources •were available, individu- als were sent multiple follow-up questionnaires in an effort to maximize the response rate. The insti- tutional review boards of the authors' universities approved this research.

The current study's sample included participants who completed the wave 2 questionnaires, in which the physical health measure was first collected. Of the basehne sample (N = 406), about 70 percent participated in the second wave (n = 285), and about 36 percent participated in the third wave (n = 146). Because the study used social workers who participated in the wave 2 questionnaire (n = 285), it was important to determine whether the study sample was similar to the initial sample and •whether the attrition •was random. As a preliminary analysis, •we conducted logistic regression models to examine the relationship among the study variahles (that is, age, gender, years in the field, income, and initial level of burnout) and the likelihood of a partici-

among Social Workers: A Three-Year Longitudinal Study 259

pant dropping out at waves 2 or 3 and found no significant relationships (results not reported here). It was therefore assumed that the study's attrition was random and that our samples were similar across all three waves.

Measures Physical Health Complaints. Physical health was measured using the Physical Health Questionnaire (PHQ) (Schat, Kelloway, & Desmarais, 2005). For this analysis, we focused on four types of physical health problems: sleep disturbances, headaches, re- spiratory infections, and gastrointestinal infections. Respondents were asked to respond to 14 questions regarding the frequency of sleep disturbances (four items), headaches (three items), respiratory infections (four items), and gastrointestinal problems (three items) in the previous six months. Responses •were made on a seven-point Likert-type scale ranging from 1 = not at all to 7 = all of the time. Scores for overall physical health and for each health problem were computed by averaging the responses, with higher scores indicating greater severity. Change scores for each respondent were computed by sub- tracting his or her wave 2 physical health score from his or her scores in wave 3. A confirmatory factor analysis conducted by Schat et al. (2005) empirically demonstrated the construct validity of the PHQ. For our sample, we obtained Cronbach's alphas of .80 for sleep disturbance, .92 for headaches, .86 for respiratory infections, and .78 for gastrointestinal problems at wave 2.

Burnout. Burnout was measured using the Maslach Burnout Inventory—Human Service Survey (MBI-HSS), a 22-item scale that conceptualizes burnout as having three dimensions: emotional exhaustion, depersonalization or cynicism, and diminished personal accomplishment (Maslach & Jackson, 1986; Maslach et al, 2001). Emotional exhaustion (nine items) is related to the worker's feelings of being overextended and depleted of emotional and physical resources. Depersonaliza- tion (five items) addresses the worker's negative or excessively detached responses to various aspects of the job. Finally, diminished personal accomplishment (eight items) reflects the worker's feelings of incom- petence and lack of achievement at work (Maslach & Jackson, 1986). For each item, respondents are asked to report the extent of their experiences along a seven-point continuum, ranging from 0 = never to 6 = every day. Although all three burnout dimen-

sions are commonly used together in the literature. Acker (1999) found that social workers working with severely mentally ill chents were more likely to experience emotional exhaustion and depersonaliza- tion but not diminished personal accomplishment. A recent study by Kim and Ji (2009) also showed that burnout among social workers may be largely explained by emotional exhaustion and deperson- ahzation. In this study, findings from a longitudinal factorial invariance test of the MBI-HSS suggested that personal accomphshment may be associated not only with burnout, but also with professional development. Therefore, for the present study, we only used the 13 items for emotional exhaustion and depersonahzation to calculate a single burnout score for each participant, leaving out the personal accomplishment items. For our sample, we obtained Cronbach's alphas of .91 for emotional exhaustion and .75 for depersonalization.

Control Variables. The analysis also controlled for other demographic and work-related characteristics that may affect burnout or physical health, including a respondent's age, gender, field tenure, and annual salary. Gender was coded as follows: Female = 1, and male = 0. Field tenure refers to the self-reported number of years a respondent had •worked as a social •worker. Annual salary •was based on a respondent's self-reported income from his or her current job (at the time of the first survey). Age, field tenure, and annual salary were all included in the analysis as continuous variables.

Analysis The analysis consisted of t̂ wo stages. First, •we ex- amined the bivariate relationships between burnout and physical health complaints. Following previous studies (for example, Lau,Yuen, & Chan, 2005), re- spondents were evenly divided into three groups on the basis of their baseline MBI-HSS scores: low [n = 92), moderate (« = 93), and high (n = 91). One- way analyses of variance (ANOVAs) were used to test whether the three burnout groups differed in their subsequent physical health complaint scores (measured at wave 2). Second, using structural equation modeling (SEM), we used path analysis to test whether basehne levels of burnout predicted changes in physical health over time with control variables accounted for. As shown in Figure 1, we modeled the change in the respondents' physical health (from wave 2 to wave 3) as being predicted by their burnout levels at •wave 1, •while account-

260

Figure 1: A Path Model of Changes in Physical Health Complaints among Social Workers

Burnout at wave 1

Annual salary

Physical health :omplaints at wave 2

Gende

Years in the field

Change in physical health complaints (wave 3-wave 2)

ing for their physical health at wave 2. Baseline demographic and •work-related characteristics were also controlled for in the model.

The one-way ANOVAs were conducted using SPSS 17.0, and the parameters for the path models were estimated •with Mplus 5.0 software (Muthén & Muthén, 2007). To estimate the path model parameters, fuU-information maximum likelihood estimation (FIML) was used to account for any miss- ing data (Arbuckle, 1996). FIML allows the estima- tion to proceed using all available data by breaking down the likehhood function into components on the basis of the patterns of missing data. SEM approaches using FIML requires data with normal distribution—absolute values of univariate skewness index greater than 3.0 and absolute values of uni- variate kurtosis index greater than 10.0 considered as problematic (Kline, 1998). For the current study's data, univariate skewness values ranged from —1.48 to 0.46, and kurtosis values ranged from -0.94 to 7.01, so practically acceptable distributions were assumed for all study variables.

Model goodness-of-fit was evaluated on the basis of several indices; the chi-square statistic divided by the degrees of freedom (x^/dfj, the comparative fit index (CFI), and the root mean square error of ap-

proximation (RMSEA). A %-/df value of less than 3 •would indicate a reasonable fit (Kline, 1998). CFI values can range firom 0 to 1, with a value above 0.90 suggesting an acceptable fit between the model and the data (Kline, 1998). Finally, an RMSEA of .05 or less would indicate a good fit (Browne & Cudeck, 1993).

RESULTS

The characteristics of our sample are summarized in Table 1. Among the 285 social workers in wave 2, 65 percent were licensed clinical social workers (LCSWs), whereas 35 percent were associate social workers (ASWs [that is, registered in the state but not yet licensed]). Almost half of the sample worked in the mental health field. At the time of the initial survey, the mean age of the sample was 46 years. Participants had worked in the social work field for an average of 17.4 years and earned an average annual income of $57,100. For the total sample, the average physical health complaints score decreased marginally from 39.6 in wave 2 to 39.5 in wave 3.

The means, standard deviations, and correlation coefficients for the study variables are presented in Table 2. Physical health was positively associated with burnout experience (r = .50) but negatively

.:th among Social Workers: A Three- Year Longitudinal Study 261

Table 1: Description of the Sample

characteristic

Wave(W)

Sample

Gender (%)

Male

Female

License status (%)

Associate social worker

Licensed clinical social worker

Service field (%)"

School social work

Child welfare/family

Healthcare

Mental health

Mean age (in years)

Mean years in the field

Mean annual salary (in thousands of dollars)

Mean level of hurnout

Mean physical health complaints scores'"

Sleep disturbances

Headaches

Gastrointestinal prohlems

Respiratory infections

Overall physical health

Mean change in physical health (W3 - W2)

406 285 146

19.9

80.1

36.7

63.3

6.9

17.0

21.7

46.1

45.6 (12.0)

17.1 (10.6)

57.0 (17.0)

31.3 (14.3)

21.7 78.3

35.4

64.6

6.0

16.5

21.4

48.4

46.0(11.9)

17.4 (10.4)

57.1 (17.1)

29.9 (13.9)

13.3 (4.8)

9.1 (4.6)

10.1 (5.2)

7.1 (3.3)

39.6(13.3)

21.0 79.0

33.6

66.4

7.5

17.1

22.6

47.3

46.1 (11.3)

18.0 (10.0)

59.9 (16.1)

31.1 (14.3)

13.7 (4.5)

8.4 (4.4)

10.0 (5.1)

7.3 (3.3)

39.5 (12.9)

-0.8 (8.7) Notes: The sample for the present consisted of the 285 social workers who participated in the W2 survey. Where means are reported, standard deviations are included in paren- theses. Burnout scores were calculated by summing participants' responses to 13 questions on a seven-point scale (coded as 0 to 6), with total scores ranging from 0 to 78. Scores for overall physical health complaints were calculated by summing participants' responses to 14 questions on a seven-point scale (coded as 1 to 7), with total scores ranging from 7 to 98. The scores for each physical health complaint were calculated in a similar manner, but their ranges varied on the basis of the number of items for each subscale: sleep disturbances (four questions; scores ranged from 4 to 28); headaches (three questions; scores ranged from 3 to 21); gastrointestinal problems (three questions; scores ranged from 3 to 21); respiratory infections (four questions; scores ranged from 4 to 28). 'Totals equal more than 100 percent because the participants were allowed to select multiple responses. Participants indicated their areas of praaice by using the categorizations of service area developed for the NASW Center for Workforce Study (Whitaker, Weismiller, & Clark, 2006). ''Physical health was not assessed at W l .

associated with age (r = —.21) and years in the field (r = -.25). In addition, burnout was significantly- correlated with age (r= -.19) and years in the field (r=-.16).

The one-way ANOVA results (see Table 3) showed significant relationships between burnout and overall physical health and between burnout and each individual health problem. In general, physical health problems were the least severe among social workers with low burnout levels and the most severe among those with high burnout levels. For overall physical health, the mean score was the lowest for social workers with low burnout levels (31.7), gradually increased for the moderate burnout group (39.7), and was even higher for the high burnout group (47.8). Similar patterns were also found for

each health problem. Results from Tukey post-hoc analyses also revealed significant low versus moderate and moderate versus high group differences: The high-burnout group was significantly worse than the moderate group, which, in turn, was significantly worse than the low group, for overall physical health and for each individual physical health problem.

The final path model, including only the signifi- cant parameters, is shown in Figure 2. We initially estimated all the parameters in our model and found that neither age nor annual salary significantly in- fluenced physical health at wave 2 or the change in physical health from wave 2 to wave 3. To derive the most parsimonious model, we removed the non- significant paths, but only if their removal did not significantly influence the overall model fit (on the

262

Variable

Table 2: Correlation Coefficients, Means, and Standard Deviations for Observed Variables

4

l.Wage

2. Gender

5.Ag,e

4. Years in the Field

5. Burnout at Wl

6. PHC (W2)

7. Change in PHC (W3 - W2)

M

SD

1.00

-.28*

.23*

.34*

.05

-.09

.13

57.07

17.12

1.00

-.23*

-.28

.01

-.17

-.01

0.78

0.41

1.00

.70*

-.19*

-.21*

-.03

46.02

11.88

1.00

-.16*

-.25*

.05

17.45

10.42

1.00

.50*

.05

30.96

14.36

1.00

-.34*

39.59

13.27

l.ÓO

-0.82

8.71 Note: Scores for overall physical health complaints (PHC) were caiculated by summing participants' responses to 14 questions on a seven-point scale (1 to 7), v^ith totai scores ranging from 7 to 98. Scores for change in overall PHC were caiculated by subtracting PHC scores at wave 2 from PHC scores at wave 3. A positive change in PHC score indicates an increase in PHC over time, w = wave, •p < .05.

basis of nonsignificant chi-square difference tests). This model trimming process is discussed in greater detail in KUne (1998).This process led to our final path model, which was a good fit to the data {"/^/df = 0.57, RMSEA = .00).This model explained 29 percent of the variance in physical health at •wave 2 and 18 percent of the variance in the change in physical health fiom wave 2 to wave 3.

The path analysis results showed that burnout was positively associated with both the initial physical health complaints (ß = .47) and the change in physi- cal health complaints from wave 2 to wave 3 (ß = .29), even after initial physical health was controlled for. In other words, higher levels of burnout led to worse physical health. Moreover, social workers with higher levels of burnout experienced a greater

Table 3: Physical Health Complaints and Burnout among Social Workers: One-Way Analysis of Variance Results

Physical Health Complaints'

Sleep disturbances

M

SD

Headaches

M

SD

Gastrointestinal problems

M

SD

Respiratory infections

M

SD

Overall physical health

M

SD

Low (n = 92)

11.2

3.9

7.0

3.8

7.9

3.5

5.7

2.3

31.7

9.4

Level of Burnouf

Middle (n = 93)

13.3

4.3

9.3 4.1

10.4

4.9

7.0

3.0

39.7

10.6

High (n = 91)

15.6

5.0

11.1

5.0

12.1

5.9

8.8

3.7

47.8

UA

Hcffe) 1 23.4(2, 273)*

20.9(2, 271)*

"ÜB 17.6(2, 272)*

"A 23.8(2, 273)*

•m

44.1(2,272)*

m Notes; Scores for overall physical health complaints were calculated by summing participants' responses to 14 questions on a seven-point scale (coded as 1 to 7), with total scores ranging from 7 to 98. Scores for each physical health complaint were calculated in a similar manner, but their ranges varied on the basis of the number of items for each subscale: sleep disturbances (four questions; scores ranged from 4 to 28), headaches (three questions; scores ranged from 3 to 21), gastrointestinal problems (three questions; scores ranged from 3 to 21), and respiratory infections (four questions; scores ranging from 4 to 28). All Tukey post-hoc tests for low versus middle and middle versus high levels of burnout were significant at p < .05. 'Assessed at wave 2. "•Assessed at wave 1. ' p < .01.

.^^.a/j among Social Workers: A Three-Year Longittidinal StuAy 263

Figure 2: Final Model of Changes in Physical Health Complaints among Social Workers

Burnout at wave 1

Female (versus male)

.47*

Physical health complaints at wave 2

- .50*

Change in physical health complaints (wave 3—wave 2)

Years in the field

- .14*

Notes: Results are reported as standardized path coefficients (ßs). Model fit: x'(6, N = 285) = 3.44. Comparative fit index = 1.00, root mean square error of appro: (Pelóse = .94). •p < .05.

deterioration in physical health over tiine. Female social workers were less healthy than their male counterparts (ß = .12). More years in the field was associated with better initial physical health (ß = —.14). Additional path analyses (not shô wn) were conducted for each separate physical health problem and showed that burnout was associated with signifi- cant increases in headaches (ß = .23),gastrointestinal problems (ß = .20), and respiratory infections (ß = .19) but not •with sleep disturbances.

DISCUSSION This study revealed that burnout can adversely affect the physical health of social workers, with higher levels of burnout leading to more physical health problems one year after initial assessment. More im- portant, social •workers with higher levels of burnout also experienced a greater decHne in overall physical health over a one-year period. Specifically, •we also found that social •workers with higher initial levels of burnout reported more headaches, gastrointestinal problems, and respiratory infections a year later.

Given the prevalence of burnout in the social work profession, these findings may have serious implications for social work practice. Health prob- lems may negatively affect the relationship between

social ^vorkers and their clients; more specifically, such problems may hinder them from developing nurturing alliances •with their chents. In addition, poorer physical health can lead to diminished job performance, including absenteeism and turnover (Darr & Johns, 2008) .Turnover among social work- ers has been found to negatively affect the quality, consistency, and stability of services (Mor Barak et al, 2001).

Although the current study focused on the physi- cal health symptoms of social workers, the potential linkages between physical and mental health cannot be ignored. Schat et al. (2005) found that physical health symptoms are significantly associated with psychological health, as measured by the General Health Questionnaire (GHQ) (Banks et al., 1980). The GHQ is a widely used measure of mental health in occupational settings and consists of items relat- ing to depression and self-confidence (Schat et al, 2005).This further suggests that burnout can result in poorer occupational •well-being for social workers in general, thus greatly compromising the quality of services provided to clients.

Our findings support the need for both burnout prevention and recovery interventions. To date, burnout intervention studies have primarily focused

264 V 2011

on preventive efforts. For example. Maslach et al. (2001) emphasized educational interventions to enhance the ability of workers to cope with stress. Similarly, S. H.Jones (2007) argued that social work education programs should increase awareness of burnout symptoms and teach students about strat- egies to prevent burnout, such as communication techniques and coping skills. Organizations can take more proactive steps to address the burnout issue (for example, increasing employee awareness of burnout, its symptoms, and its effects; developing preventive strategies).

In particular, managers and supervisors must play a critical role in supporting their staff and preventing burnout. Research has shown that supervisor support and performance feedback are essential to preventing worker burnout (Bakker, Demerouti, & Euwema, 2005). Supervisors who are open and responsive to the opinions of fronthne social workers about their job-specific problems can help those workers to cope with job demands (Kim & Lee, 2009). In a similar vein, Himle andjayaratne (1991) found that instrumental support from a supervisor helped to buffer the influence of job stress on burnout. Direct supervisors and managers may play an important "bridging" role between their staff and agency ad- ministrators, helping to identify burnout symptoms among their staff and communicating these difficul- ties to administrators. Relatedly, effective supervision has been identified as a key factor in the retention of social workers, pubHc child welfare workers, and human service workers in general (Chenot,Benton, & Kim, 2009; Mor Barak,Travis, Pyun, & Xie, 2009; Rycraft, 1994).

In addition to supervisor support, support from coworkers, peers, and spouses have been shown to be critical in helping to prevent burnout among social workers. For example, Himle and Jayaratne's (1991) study showed that informational support among coworkers softened the effects of role conflict and workload on the risk of emotional exhaustion among social •workers. Davis-Sacks, Jayaratne, and Chess (1985) found that increased spousal support may buffer the risk of depersonalization among female child welfare workers. Future research could also focus on the potential role of familial and community support in alleviating burnout among social workers.

Also critical, but less understood, are burnout recovery interventions. Social ^vorkers are argu- ably at a high risk of burnout because of intense

worker—client interactions and unmanageable case- loads. Therefore, to mitigate any potentially adverse effects of burnout, recovery interventions would be necessary to assist social workers •who have or are experiencing burnout and to help them cope with their current burnout experiences. Again, ad- ministrators and supervisors may play an important role in supporting and empowering social workers to sustain their commitment to the job. Self-help groups or social networks may also provide social workers with spaces in which to share their experi- ences and support each other. Easily implemented screening tools and interventions could be devel- oped and targeted to social workers in settings that may pose the greatest risk of burnout (for example, CPS workers, workers serving severely mentally iU populations) .The feasibihty and cost-effectiveness of potential burnout recovery programs are important topics for fiiture research.

Our results also showed that number of years in the field was negatively associated with physical health complaints, suggesting that entry-level or early-career social •workers are particularly at risk. This is consistent •with earlier studies, which have shown that human service workers who are begin- ning their careers are more hkely to experience burnout (Maslach et al., 2001). Moreover, on the basis of an extensive review on the burnout litera- ture, Schaufeli and Enzmann (1998) concluded that burnout, if not addressed early on, tends to persist. Similarly, Poulin and Walter (1993b) conducted a longitudinal survey of 879 social workers and found that burnout is a stable phenomenon among professional social workers. Therefore, prevention and recovery interventions in the early stages of a social worker's career (including while he or she is a student) could help to improve his or her long- terni health (and, potentially, career) trajectories. Both baccalaureate- and graduate-level social work programs—via their curriculums, alumni networks, and continuing education programs—could assist students and recent graduates in handhng burnout early in their careers and, thus, protect their health in the long term.

Gender emerged as another significant predictor of physical health complaints, with female social workers reporting significantly worse physical health than their male counterparts. This gender differential is consistent with findings in the lit- erature (Haug, Mykletun, & Dahl, 2004) and may have several explanations. For instance, it is possible

•* among Social Workers: A Three-Year Longitudinal Study 265

that women have poorer health and exhibit more symptoms than do men, resulting in their report- ing more symptoms (Barsky, Goodson, Lane, & Cleary, 1988). Another possible explanation is the higher prevalence of anxiety and depression found among women, which has been shown to be as- sociated with increased reporting of physical health problems (Haug et al.,2004;Wool & Barsky, 1994). Considering that the majority of social workers are female, this finding has significant imphcations.

It is interesting to note that age •was found to be a negative determinant of physical health in the bivariate analysis but not in the path analysis. Age has been consistently found to be a negative correlate of burnout (Brewer & Shapard, 2004), so one possible explanation for the negative relationship between age and physical health is that younger (and often less experienced) workers are more susceptible to burnout and, thus, exhibit poorer physical health. For example, in an extensive reviê w of the litera- ture on burnout and cardiovascular-related events, Melamed et al. (2006) found that the relative risk associated with burnout was equal to, and sometimes even exceeded, the risk associated •with classical risk factors such as age, smoking, and body mass index.

The present study has several key limitations. First, the analysis was based on a sample of registered social workers in California, which may Hmit the gener- alizability of the findings to social •workers in other parts of the United States. Nationally representative studies could further advance our understanding of the association between burnout and physical health in social work. Second, our findings may also be biased due to sample attrition. We tried to address this concern •with a preliminary attrition analysis, which determined that the attrition •was unrelated to our key study variables. In addition, by using FIML to estimate our models, •we were able to include information for the entire sample—including those participants who left the study.Third, we were only able to follow social workers for three years, collect- ing information at one-year intervals. It is possible that three years is not sufficiently long enough a period to assess changes in workers' physical health. Furthermore, the participants' initial physical health status was not collected, so information about their physical health was only available for two time points. Finally, the current study did not account for a broad array of additional factors that may affect a person's health, such as health behaviors, life events or Stres- sors, genetic influences, and accidents.

Nonetheless, this study presents compelling evidence of the significant impact of burnout on physical health in the social work profession. The findings also highlight the critical need for social workers to pay attention to their own occupational well-being and, more broadly, the need for the social work profession to focus more resources on understanding and addressing the impact of burn- out. To date, our understanding of burnout and its potential health consequences for social workers has been hmited. Moreover, there is a tremendous need for research on effective burnout interventions. Future research should explore how physical health may affect the job performance of social •workers, including their decision making and job turnover. At the same time, •we need to identify factors that may ameliorate the adverse effects of burnout on physical health among social workers.

Finally, this study highlights the critical need for the social •work profession and all of its stakehold- ers (for example, members, educational programs, service agencies) to deal proactively •with burnout. Social workers are among the ^vorkers most at risk when it comes to experiencing burnout at some point in their careers (for example, Priebe, Fakhoury, Hoffmann, & Powell, 2005; Siebert, 2005). As our findings suggest, burnout can lead to a worsening of one's health over time, which, in turn, can com- promise the quality of the services one provides to chents.

According to the NASW (2008) Code of Ethics, social workers are called on to promote social justice and social change by addressing social inequality through the empo^werment of clients, promotion of cultural diversity, and resistance to social injustice. In particular, social •workers are committed to address- ing the needs of vulnerable and underprivileged children and families. However, in the process, social workers find themselves at risk in terms of their health and well-being. If action is not taken to counteract the prevalence of burnout and its impact on the psychological and physical well-being of its members, our profession •wül continue to struggle to sustain a strong and vibrant pool of social work- ers. Therefore, it is critical to build healthy working environments in which the next generation of social workers can meet the emerging social challenges of the 21st century.

One such current effort is the Dorothy I. Height andWhitney M.Young,Jr. SocialWork Reinvestment Act (H.R. 795 and S. 686), which is comprehensive

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federal legislation aimed at promoting recruitment, retention, research, and reinvestment in the profes- sion of social work (see NASW, 2009). If passed, it would help to improve the working conditions of professional social workers. For example, it ^would fund demonstration programs on improving the workplace, addressing, among other conditions, high caseloads, fair market compensation, social worker safety, supervision, and working conditions—all of which are significant factors in burnout and turnover among social workers (Chenot et al., 2009; Kim & Stoner, 2008; Mor Barak et al., 2001,2009; Siebert, 2005). If we are unable to find ways to protect the occupational well-being of the current workforce, worker shortages wül only become more serious. Our hope is that this study will help to inform the development and the enactment of policy measures, such as the Social Work Reinvestment Act, focused on securing both federal and state investment in the social work profession.

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Hansung Kim, PhD, is assistant professor. Department of Sociology, Hanyang University, 222 Wangsimni-ro, Seongdong- gu, Seoul 133-191, Korea; e-mail: [email protected] Ji, PhD, is assistant professor. School of Social Work, Syracuse University, Syracuse, NY. Dennis Kao, PhD, is assistant professor. College of Social Work, University of Houston. This research was supported in part by a research award from the Hamovitch Center for Science in the Human Services, an Albert and Frances Feldman Endowed Fellowship from the Univer- sity of Southern California School Social Work, and a faculty development grant from California State University, FuUerton. The authors thank all of the social workers who participated in this longitudinal study for their patience and support. An earlier version of this article was presented at the Í2th annual confer- ence of the Society for Social Work and Research, January 19, 2008, Washington, DC.

Original manuscript received January 5, 2009 Final revision received October 13, 2009 Accepted March 25, 2010

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