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Burnout in human service work
Causes and consequences
AMI 2006. Phd. Afhandling. 63 s.
Bogomtale fra forlaget.
This thesis summarizes the results of the PhD-project “Burnout in human service work – causes and consequences” carried out during the period March 2003 to February 2005 at the National Institute of Occupational Health, Copenhagen, Denmark. The project had two major aims: 1) to investigate possible causes for burnout, and 2) to evaluate burnout as a predictor for sickness absence. Burnout is a "grassroots" concept introduced in the 1970’s as a particular type of prolonged occupational stress that seemed to occur most prominently among human services professionals, with emotional exhaustion as its core symptom. Until start of the new millennium, little was known about causes and consequences for burnout because most studies were cross-sectional.
Further, many burnout questionnaires can only be used in the human service section and the measure of exhaustion is confounded with measures of potential causes and consequences of exhaustion. These problems limit the usefulness of existing questionnaires. For these reasons, a new instrument, the Copenhagen Burnout Inventory (CBI), was developed and it psychometric properties was evaluated as part of this study. Data for the PhD-project are based on questionnaire data and stems from baseline (n=1,914) and 3-years follow-up (n=1,024) of the PUMA study, an ongoing six-year prospective intervention study in the human services sector. Burnout was measured with a new instrument, the Copenhagen Burnout Inventory (CBI), whose psychometric properties are evaluated as part of the study. As potential causes of burnout, the study evaluated psychosocial work environment factors, which were measured with the Copenhagen Psychosocial Questionnaire (COPSOQ) and with some additional items on client-related work. Sickness absence was measured by selfreported number of sickness absence days and spells during the last 12 months before the baseline and the follow-up survey. Linear regression models and Poisson regression models were used for the analyses.