Factors Associated with Cliniciansâ Recommendation for Return to Work in Patients with Work-related Shoulder and Elbow Injury
Bibliographic record
Abstract
Background: RTW after work-related injuries is a multifactorial process. Factors affecting clinicians to make RTW-recommendations for patients with WRSEI have not been studied in the literature.\n\nPurpose: We investigated the associations between group of factors chosen from different domains (Personal/Environmental) and clinicians’ RTW-recommendations for patients with WRSEI.\n\nMethods: Study design was cross-sectional. Data were collected from self-reported surveys and clinical charts of 130 adult workers (not working at the time of visit and referred to WSIB-Shoulder & Elbow Specialty Clinic-Toronto) with chronic (≥6-months) injuries.\n\nResults: Population mean age was 43.5-years. 52% were female. The average time-since-injury was 20.4-months (45%>12-months). 70% received RTW-recommendations (regular/modified-job). 30% received a No-RTW-recommendation. 42% had education≥college-level. 18% had heavy (>20kg) job-demands. Higher MCS-scores had a significant association (p=0.0003) with clinicians’ RTW-recommendations.\n\nConclusion: In patients with chronic WRSEI(s), poor general health-status and high disability, workers with better mental-health were more likely to receive a RTW-recommendation by clinicians.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".