Return-to-work for multiple jobholders with a work-related musculoskeletal disorder: A population-based, matched cohort in British Columbia
Bibliographic record
Abstract
INTRODUCTION: Multiple jobholders (MJHs) have a higher risk of injury compared to single jobholders (SJHs), but it is unknown if return-to-work (RTW) after a work injury is affected by multiple jobholding. This study examined the association between multiple versus single jobholding and time to RTW for workers with a work-related musculoskeletal disorder (MSD). METHODS: We used administrative workers' compensation data to identify injured workers with an accepted MSD lost-time claim between 2010-2014 in British Columbia, Canada (n = 125,639 SJHs and 9,029 MJHs). The outcome was days until RTW during twelve months after the first day of time-loss. The MJH and SJH cohorts were balanced using coarsened exact matching that yielded a final matched cohort of 8,389 MJHs and 8,389 SJHs. The outcome was estimated with Cox regression, using piecewise models, and the hazard ratios were stratified by type of MSD, a serious injury indicator, gender, weekly workdays preceding MSD, and wage categories. RESULTS: MJHs were less likely to RTW compared to SJHs within the first six months after the first time-loss day, with greater and longer lasting effects for males, workers with a serious injury, and a higher wage. No difference between MJHs and SJHs was found for workers who had a six- or seven-day work week preceding MSD, for workers with dislocations, and for workers who were still off work after six months. CONCLUSIONS: Overall, MJHs with a workweek of maximum five days are disadvantaged compared to SJHs in terms of RTW following a work-related MSD within the first six months after the first time-loss day. This difference might be caused by more precarious job contracts for MJHs that challenges RTW because of lack of support for modified work, higher workload, and reduced likelihood that MJHs file a workers' compensation claim. Despite adjusting for type of MSD, severity of injury and occupation, the differences persisted for the vast majority of the study sample.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".