AI Article Synopsis
- - The study aimed to identify and rank various individual and job-related factors that contribute to different lengths of sick leave (very short, short, and long-term).
- - Data were gathered from surveys in the French private sector between 2011 and 2017, and a conditional random forest method was used to rank 51 determinants of sick leave.
- - Key findings revealed that long-term sick leaves were influenced primarily by health and work-related factors, while very short leaves were more associated with sociodemographic characteristics.
Article Abstract
Objective: We hierarchized a range of individual and occupational factors impacting the occurrence of very short (1-3 days), short (4 days to 1 month), or long-term (more than a month) sick leave spells.
Methods: Data were collected from a repeated cross-sectional survey conducted in the French private sector over the period 2011 to 2017. Fifty one sick leave determinants were ranked using a conditional random forest approach.
Results: The main determinants of long-term sick leaves were mainly health-related characteristics, such as perceived health, but also work-related covariates such as supervisor acknowledgment. On the contrary, very short-term spells were mainly defined by sociodemographic covariates.
Conclusion: These results could be useful for devising appropriate actions to prevent against sick leave at the workplace, particularly long-term spells. Random forest approach is a promising approach for ranking correlated covariates from large datasets.
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| http://dx.doi.org/10.1097/JOM.0000000000001643 | DOI Listing |
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