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• W2966385311 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." @default.
• W2966385311 created "2019-08-13" @default.
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• W2966385311 date "2019-08-01" @default.
• W2966385311 modified "2023-10-15" @default.
• W2966385311 title "Hierarchizing Determinants of Sick Leave" @default.
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• W2966385311 doi "https://doi.org/10.1097/jom.0000000000001643" @default.
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