AIM: To test the strain/iso-strain, interaction and buffer hypotheses of the Job Demand-Control-Support model in relation to anxiety and depression. METHODS:Five thousand five hundred and sixty-two workers with valid Demand-Control-Support Questionnaire (DCSQ) scores were examined with the sub-scales of the Hospital Anxiety and Depression Scale as outcomes. Multiple statistical methods were applied. RESULTS: The strain and iso-strain hypotheses were confirmed. Generally, additive and non-interaction effects were found between psychological demands, control and social support. The buffer hypotheses were refuted. Results from analyses testing different interaction operationalizations were complementary. CONCLUSIONS: High demands, low control and low support individually, but particularly combined, are risk factors for anxiety and depression. Support is the DCSQ index most strongly associated with anxiety and depression in women. Assessment of psychosocial work environment may identify workers at risk, and serve as a basis for job-redesign.
RCT Entities:
AIM: To test the strain/iso-strain, interaction and buffer hypotheses of the Job Demand-Control-Support model in relation to anxiety and depression. METHODS: Five thousand five hundred and sixty-two workers with valid Demand-Control-Support Questionnaire (DCSQ) scores were examined with the sub-scales of the Hospital Anxiety and Depression Scale as outcomes. Multiple statistical methods were applied. RESULTS: The strain and iso-strain hypotheses were confirmed. Generally, additive and non-interaction effects were found between psychological demands, control and social support. The buffer hypotheses were refuted. Results from analyses testing different interaction operationalizations were complementary. CONCLUSIONS: High demands, low control and low support individually, but particularly combined, are risk factors for anxiety and depression. Support is the DCSQ index most strongly associated with anxiety and depression in women. Assessment of psychosocial work environment may identify workers at risk, and serve as a basis for job-redesign.
Authors: Ruben A Kraaijeveld; Maaike A Huysmans; Marco J M Hoozemans; Allard J Van der Beek; Erwin M Speklé Journal: Int Arch Occup Environ Health Date: 2013-02-27 Impact factor: 3.015