Factors associated with unsuccessful return-to-work following work-related upper extremity injury
Bibliographic record
Abstract
BACKGROUND: Returning to work following occupational injury is a key outcome for both workers' compensation boards and injured workers. Predictive factors for returning remain unclear. AIMS: To describe factors associated with unsuccessful return-to-work (RTW) in a hand injury population to identify target areas through which occupational rehabilitation programmes can help injured workers achieve successful RTW outcomes. METHODS: Demographic data, functional, pain and psychosocial scores were recorded for injured workers discharged between April 2011 and September 2015 from a multidisciplinary upper extremity treatment programme. The primary outcome of RTW status was assessed at programme discharge. Bivariate analyses and multivariable logistic regression were used to identify factors associated with being unable to RTW. RESULTS: Of 872 participants who met the inclusion criteria, 65% were male and the mean age was 46 (standard deviation [SD] 11) years. In unadjusted bivariate analyses, the group with an unsuccessful RTW outcome had higher mean baseline pain, catastrophizing and QuickDASH scores; a higher baseline prevalence of depression, and reported a high level of pain more frequently than those who were working at discharge. In the adjusted logistic regression model, not working at baseline, higher QuickDASH score and presence of depression at baseline were independently associated with unsuccessful work status outcome. CONCLUSIONS: Negative baseline work status, greater self-reported functional disability and presence of depression were associated with greater odds of unsuccessful RTW following a workplace upper extremity injury. Integrating mental healthcare provision with occupational rehabilitation is a potential programmatic approach to improve RTW.
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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 | 0.007 |
| 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 | 0.001 | 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".