Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis

Abstract

This study examines the association of sleep quality with job burnout among Chinese coal mine staff. 3832 subjects were selected from a coal mine group located in Shanxi Province in China. Job burnout was evaluated by the Maslach Burnout Inventory-General Survey and sleep quality was acquired with a self-reported questionnaire. We used the inverse probability of treatment weighting with propensity score to mimic the randomization and to minimize bias in estimations. Sensitivity analysis was conducted to test the robustness of our findings. We identified that good sleep quality was significantly associated with lower risk of job burnout (OR: 0.70; 95%CI, 0.60 to 0.82, p = 6.02e-06), with 0.21 decrease in the score of exhaustion (95%CI,−0.29 to −0.12, p = 5.00e-06), and with 0.13 decrease in the score of cynicism (95%CI,−0.21 to −0.04, p = 3.73e-03). Sensitivity analysis demonstrated that the results were robust to the choice of estimation models, as well as unmeasured confounding. Stratification analysis demonstrated that the associations of sleep quality with job burnout were largely heterogeneous for male and female workers. This study implicated that good sleep quality benefits the workers in relief of job burnout. Further research may be warranted in support of a definite causal relationship and intervention strategy.

Sleep and health: examining the relation of sleep to burnout and well-being using a consumer fitness tracker

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“…Furthermore, literature shows that disturbed or non-restorative sleep also influences one of the most prominent health issues in the last decade: burnout [1,13,14]. Metlaine et al [14] found subjective sleep data (insomnia and non-restorative sleep) to be associated with a higher risk of burnout.…”

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“…It can transform the qualitative evaluation of the problem into the quantitative evaluation, and can better solve the difficult to quantify safety evaluation problems. Its basic step is to use grey relation analysis [18][19][20][21] to determine the weight coefficient [22][23][24] of each influencing factor in the evaluation index system, use the expert scoring method to obtain the fuzzy evaluation matrix, calculate the correlation degree of the index, and finally carry on the fuzzy operation to get the fuzzy evaluation result. This method is simple in calculation, clear in result, and has high feasibility and credibility.…”