Predicting Return to Work in Workers with All-Cause Sickness Absence Greater than 4 Weeks: A Prospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Long-term sickness absence is a major public health and economic problem. Evidence is lacking for factors that are associated with return to work (RTW) in sick-listed workers. The aim of this study is to examine factors associated with the duration until full RTW in workers sick-listed due to any cause for at least 4 weeks. METHODS: In this cohort study, health-related, personal and job-related factors were measured at entry into the study. Workers were followed until 1 year after the start of sickness absence to determine the duration until full RTW. Cox proportional hazards regression analyses were used to calculate hazard ratios (HR). RESULTS: Data were collected from N = 730 workers. During the first year after the start of sickness absence, 71% of the workers had full RTW, 9.1% was censored because they resigned, and 19.9% did not have full RTW. High physical job demands (HR .562, CI .348-.908), contact with medical specialists (HR .691, CI .560-.854), high physical symptoms (HR .744, CI .583-.950), moderate to severe depressive symptoms (HR .748, CI .569-.984) and older age (HR .776, CI .628-.958) were associated with a longer duration until RTW in sick-listed workers. CONCLUSIONS: Sick-listed workers with older age, moderate to severe depressive symptoms, high physical symptoms, high physical job demands and contact with medical specialists are at increased risk for a longer duration of sickness absence. OPs need to be aware of these factors to identify workers who will most likely benefit from an early intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".