Identifying cut-off scores for job demands and job control in nursing professionals: a cross-sectional survey in Germany

Objectives

Nurses are generally found to be vulnerable to burnout, but nurses working in cancer care are even more so, since this profession is characterised by continuous confrontation with suffering and death. This study was designed to identify cut-off scores for job strain, that is, low job control and high job demands, for a sample of nurses working in breast cancer care. The main goal was to find cut-off scores, which predict the risk of nurses of developing a mental disorder from high job strain.

Design

The design was a cross-sectional survey study.

Setting

The study is based on an employee survey in breast cancer centres in Germany.

Participants

688 nurses received a questionnaire; 329 nurses from 33 hospitals participated in the survey (return rate: 50.2%).

Primary and secondary outcome measures

Dependent variable: psychological well-being, measured by the WHO-5 Well-being Index; independent variables: job control and job demands, measured by the Job Content Questionnaire (JCQ).

Results

Multivariable analysis indicates that low job control and high job demands are prognostic factors for low well-being. In a receiver operating curve (ROC) analysis, the cut-off scores, which demonstrated a maximum Youden index, were 34.5 for job control and 31.4 for job demands. The combination of both scales from a logistic regression analysis resulted in an area under the curve of 0.778. Sensitivity and specificity are 70.3% and 74.2%, respectively. The total of correct classification was 63.3%.

Conclusion

The determined cut-off scores indicate that there is a risk of becoming psychologically ill from a high workload when an individual reaches a score of ≤34.5 for job control and ≥31.4 for job demands. The described method of establishing risk-based cut-off scores is promising for nursing practice and for the field of occupational health. Transferability and generalisability of the cut-off scores should be further analysed.

https://ift.tt/2L7pKI1