Abstract

Single-item measures have become popular for the assessment of job stressfulness among researchers and practitioners keen to limit assessment burden and interruption to work activities, while maximising response rates in psychosocial risk assessment. A typical single-item measure invites respondents to indicate the degree to which they find their job stressful on a 5-point scale of not at all, mildly, moderately, very, and extremely, with responses of 4 or 5 being indicative of high job stressfulness. Despite the popularity of this measure with practitioners there remains a paucity of validation evidence. This validation study aimed to determine the extent to which a response of 4 or 5 was associated with self-reported cases of common mental disorder (CMD). Police officers from two English county forces completed a self-report questionnaire to report their overall job stressfulness, psychological distress, and burnout. We established associations between high job stressfulness and CMD cases using binary logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Scores on the three measurement instruments were obtained from 1226 officers (48% response rate). Forty per cent of respondents reported high job stressfulness, 52% high psychological distress (PD), 51% high emotional exhaustion (EE), 47% high depersonalisation (DP), and 68% low personal accomplishment (PA). The ORs for PD (OR 8.84, CI: 6.73 to 11.60), EE (OR 11.81, CI: 8.90 to 15.66), DP (OR 2.88, CI: 2.27 to 3.64), and PA (OR 1.88, CI: 1.46 to 2.43) were significantly elevated for those reporting high job stressfulness. These findings indicate that responses to the single-item measure of job stressfulness, with a cut off drawn at ≥4, differentiated between cases and non-cases across wellbeing indices. A single-item measure of job stressfulness might represent an efficient first pass psychosocial risk assessment for the identification of areas that warrant in-depth assessment and targeted risk reduction activities.

Full Text
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