Data linkage to build detailed return-to-work trajectories for work disability research
Bibliographic record
Abstract
IntroductionMusculoskeletal disorders (MSDs) are the most prevalent chronic condition in Canada, and account for the highest disability costs. Gradual-return-to-work (GRTW) can improve health and labour market outcomes in an aging workforce at risk of MSDs. Linked longitudinal data enables us to generate evidence of GRTW to inform policy needs. Objectives and ApproachThe objective of this study was to investigate the effectiveness and cost-benefits of GRTW for workers with a work-acquired MSD in British Columbia, Canada. We linked workers’ compensation data, health services data, and prescription data from three governing bodies to 1) identify injured workers with an accepted MSD lost-time injury between 2010 and 2015; 2) identify trajectories of RTW states (injury, sickness absence, GRTW, RTW, and non-RTW) and the probability of transitioning between states; and 3) assess the association between workers characteristics and RTW trajectories, and analyze the cost-benefits of GRTW. ResultsFinal results are expected early 2019. To our knowledge, this will be the first study linking workers’ compensation data (in particular detailed RTW data), health services data and prescription data from three different governing bodies for a comprehensive, population-based investigation of work disability experiences over a longitudinal time period and within the Canadian context. Also, using this data for the purpose of assessing the cost-benefits is new, and will help to prioritize prevention resources and strategies to limit the health and economic impact of work-related MSDs on employers, workers’ compensation boards and society. Conclusion/ImplicationsEvaluating the effects of GRTW on work disability is essential to maximize the health and economic benefits for injured workers. The innovation of this project is that is links three population-based databases to capture multiple indicators of health and work status to build RTW trajectories over time.
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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 | 0.093 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".