Job-related stress and sickness absence among belgian nurses: a prospective study

J Nurs Scholarsh. 2014 Jul;46(4):292-301. doi: 10.1111/jnu.12075. Epub 2014 Apr 22.

Abstract

Purpose: The purpose of this study was to investigate the influence of job stress on sickness absence of nurses and determine the predictive power of the Demand-Control-Support (DCS) model, the Effort-Reward Imbalance-Overcommitment (ERI-OC) model, and a combination of both.

Design: A survey was conducted to measure job stress in a sample of 527 Belgian nurses, followed by prospective data collection of sickness absence (long-term, short-term, and multiple episodes).

Findings: Perceptions of job strain and ERI increased the odds for long-term (adjusted odds ratio [OR] = 2.26; 99% confidence interval [CI; 1.27-4.04]) and multiple episodes of sickness absence (adjusted OR = 1.64; 95% CI [1.01-2.65]). Iso-strain and ERI-OC increased the odds for long-term (OR = 1.75; 95% CI [0.98-3.11]), multiple episode (adjusted OR = 1.93; 95% CI [1.14-3.26]), and short-term (adjusted OR = 1.69; 95% CI [1.03-2.76]) sickness absence.

Conclusions: The combined model of DCS and ERI-OC predicts the odds for long-term and short-term sickness absence and multiple episodes.

Clinical relevance: This study has implications for human resources management in nursing organizations. Nursing administrators are advised to monitor and balance nurses' job demands and efforts. They should recognize the importance of social support, job control, job rewards, and overcommitment in order to reduce the job stress of nurses.

Keywords: Job-related stress; demand-control support; effort-reward-imbalance; overcommitment; sickness absence.

MeSH terms

  • Adult
  • Attitude of Health Personnel*
  • Belgium
  • Employment / psychology*
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Theoretical
  • Nurses / psychology*
  • Nurses / statistics & numerical data
  • Prospective Studies
  • Sick Leave / statistics & numerical data*
  • Stress, Psychological / etiology*
  • Time Factors