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Do Personal Resources Reduce Job Exit and
Burnout in Child Welfare Social Workers?
Sherrill J. Clark, Ph.DRichard Smith, MFA, MSW
SSWR 2009 New Orleans, LAUniversity of California, Berkeley
School of Social Welfare6701 San Pablo #420Berkeley, CA 94720
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Table of Contents
• Focus of the presentation
• Definition: burnout, resources
• Theories of Burnout
• Empirical literature of effect of burnout on child welfare turnover
• Burnout and turnover in Title IV-E graduates
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Focus of the Presentation
• How does burnout relate to personal resources?
• Are some personal resources protective against burnout?
• How is burnout related to turnover?
• Is all burnout pathological?
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Definitions
• Conservation of Resources: Physical objects, social conditions, personal characteristics or energy that support health (Neveu, 2007)
• Co-ethnic resources (eg. Light)• Burnout: “a syndrome of emotional exhaustion
and cynicism that occurs frequently among individuals who do ‘people-work’ of some kind” (Maslach & Jackson, 1981, p. 99).
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Theories of Burnout
• Person Environment Fit
• Job Demands
• Maslach Burnout Inventory Scale
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Person Environment Fit (PE)
Stress would occur if mismatch:
• 1) between the job requirements and worker’s abilities (DA Fit);
• 2) between supplies in the environment and worker values (SV Fit) (Edwards, 1996).
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Job Demands Theory
• Stress is a result of having a job with the following:
• high strain• low control (Van Der
Doef & Maes, 1999).
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Maslach Burnout Inventory (MBI)
• 22 Questions grouped into three factors (aka subscales):
• Emotional exhaustion (EMO),
• Depersonalization (cynicism),
• Personal accomplishment (self-efficacy).
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MBI Scale
• Maslach used factor analysis to identify these after administering over 11,000 tests to human services professionals broadly defined that included social workers, nurses, teachers, police and others (1981).
MAS10.00
MAS20.00
MAS30.00
MAS40.00
MAS50.00
MAS60.00
MAS70.00
MAS80.00
MAS90.00
MAS100.00
MAS110.00
MAS120.00
MAS130.00
MAS140.00
MAS150.00
MAS160.00
MAS170.00
MAS180.00
MAS190.00
MAS200.00
MAS210.00
MAS220.00
emo 0.00
dep 0.00
pa 0.00
Chi-Square=1263.60, df=206, P-value=0.00000, RMSEA=0.082
0.000.000.00
0.00
0.00
0.00
0.00
0.00
0.00
0.000.00
0.00
0.000.00
0.00
0.00
0.000.000.00
0.00
0.00
0.00
0.00
0.00
0.00
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MBI Ranges (Guntupalli & Fromm, 1996)
Subscale Low Moderate High
Emotional exhaustion (54 max) <18 18-29 >29
Depersonalization (30 max) <6 6-11 >12
Personal accomplishment (48 max) >39 39-34 <33
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Burnout in Social Work
• Soderfeldt et. al. (1995) reviewed 18 articles on burnout in social work.
• Only 10 of the reviewed studies reported scores and only six were comparable.
• They concluded in contrast to reviewed articles that burnout in social work was low and no cause of alarm.
• Causes of stress and organizational factors need systematic investigation before recommending policy.
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Turnover in Social Work
Found that the five largest predictors of turnover are
• role overload, • stress, • negative affectivity,
and • emotional exhaustion
and burnout.
The five largest predictors of intention to leave:
• job problems, • stress, • emotional exhaustion, • role overload • burnout (Mor Barak
et. al. 2001).
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Turnover in Child Welfare
• Zlotnik, et. al’s (2005) presents a concept map of general trends to improve retention that has three broad areas:
• personal characteristics, such as burnout prevention and being bilingual;
• organizational factors and improved training (sense of efficacy) through the Title IV-E program.
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Influences of Public Child Welfare Worker Turnover
Model
OrganizationalAttributes
Turnover
Individual Attributes
Environmental Attributes
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Turnover Rates in Child Welfare
Zlotnik includes turnover rates from two studies:
• 32% in Arkansas (2002),
• 20% in Oklahoma (Rosenthal, et. al.; 1998), and
• California (2003) reported 9.5% turnover for all counties excluding Los Angeles (CalSWEC, 2004).
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CalSWEC Retention Study
Study Design
• Secondary data analysis of data collected using a mailed exit survey collected in voluntary, repeated cross sections.
Participants
• 1001 Title IV-E MSWs from 1993- 2008.
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CalSWEC Retention Study
Procedures• Within one year of completing payback,
CalSWEC mailed a survey with two follow up mailings to eligible graduates yielding a response rate of 51%.
• Non-responders had no observed significant differences except greater than expected proportion of African-Americans and Leavers.
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CalSWEC Retention Study
Measures
• MBI (Imputed)
• Controls for age, race, gender, partnership status.
• Implicit control for education level.
• Salary, year, region, school considered as controls but dropped for parsimony.
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CalSWEC Retention Study
Data Analysis
Do these IV-E graduates report burnout after two years?
What are the differences in odds of job exit for different racial and ethnic groups?
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IV-E Graduates After Payback (N=1001)
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Emotionally Exhausted
Depersonalized
Lacks PersonalAccomplishment
High RangeLow or Moderate RangeMissing
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Stayers (N=807; 82%) vs. Leavers (N=180; 18%)
0 5 10 15 20 25 30 35 40
Emotional Exhaustion
Depersonalization
Personal Accomplishment
Stayer MeanLeaver Mean
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Comparing Mean Scores of Workers Over 40 to Under 40 (N=987)
0 5 10 15 20 25 30 35 40
Emotional Exhaustion
Depersonalization
Personal Accomplishment
Over 40 Years OldUnder 40 Years Old
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MBI Subscale Means by Race and Bilingual
me
ssu
m +
md
psu
m +
mp
asu
m
0
10
20
30
40
50
Monolingual Bilingual
Black
Monolingual Bilingual
American Indian
Monolingual Bilingual
Asian American
White Hispanic/Latino
0
10
20
30
40
50
Other/Mixed
messummdpsummpasum
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Effect of Burnout Subscales on Job Exit (N = 987)
DV PA SE DP SE EE SE
Over 40 2.54 *** 0.48 -1.83 *** 0.41 -1.59 ** 0.80
Bilingual -0.99 * 0.53 -0.22 0.45 0.52 0.89
Female -1.50 ** 0.60 -0.91 * 0.50 2.04 ** 1.00
Partner 0.55 0.44 0.18 0.37 0.24 0.74
Native 0.74 1.73 1.11 1.46 2.73 2.89
Other 0.73 0.91 0.93 0.77 1.32 1.52
Asian -0.93 0.99 2.17 *** 0.84 2.11 1.67
Hispanic 0.11 0.82 0.36 0.69 -0.72 1.37
White 1.27 * 0.69 1.75 *** 0.58 0.99 1.16
N 981 979 977
R^2 0.05 0.04 0.01
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Effect of Burnout Subscales on Job Exit (N = 987)
Job Exit Odds Ratio Std. Err.
Emotional Exhaustion 1.017 0.011
Depersonalization 1.012 0.020
Over 40 0.839 0.163
White 1.554 0.451
Asian 1.219 0.470
Hispanic 1.499 0.464
Other 2.237 ** 0.775
Native 3.336 ** 1.905
EE X Over 40 1.029 * 0.017
DP X Hispanic 1.089 ** 0.038
EE + EE X Over 40 1.047 *** 0.015
DP + DP X Hispanic 1.102 *** 0.036
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Results
Being female reduces depersonalization and increases personal accomplishment and emotional exhaustion. Being over 40 reduces depersonalization and emotional exhaustion, but lowers personal accomplishment. The effect of being bilingual of increasing personal accomplishment one point on average is approaching significance.
There is no significant protective effect for being bilingual having a partner, gender or age on job exit.
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Results
• Burnout increases job exit for those over 40 who are one standard deviation above the mean in emotional exhaustion and Hispanics who are one standard deviation above the mean in depersonalization.
• Black, Asian, and some Hispanic social workers as likely to stay as whites. Native Americans have 3.3 greater odds of leaving than African Americans. Those who identify as multiracial or other race have 2.2 times the odds of leaving as Blacks. Because 23% speak Spanish, they probably are Latino.
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Conclusion
• Consistent with previous research, social workers experience emotional exhaustion, but this does not lead to job exit all things being equal. Personal resources do not appear to protect from job exit in this sample, unless they all equally benefit from MSW IV-E training.
• Older and arguably more experience social workers who are also burnt out probably feel they do not have time to spend in an emotionally exhausting job. This would support theories that job exit is a healing response.
• Mixed/other race workers might be deprived of coethnic resources. One Native worker cited applying the Indian Child Welfare Act as a frustration in the job. Future research should pay attention to these populations.
• See Jacquet et. al. (2007) for the impact of the work environment, supervisor support and workload on job exit.