Determinants of return to work following non life threatening acute orthopaedic trauma: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine factors associated with return to work following acute non-life-threatening orthopaedic trauma. DESIGN: Prospective cohort study. PARTICIPANTS: One hundred and sixty-eight participants were recruited and followed for 6 months. The study achieved 89% participant follow-up. METHODS: Baseline data were obtained by survey and medical record review. Participants were further surveyed at 2 weeks, 3 and 6 months post-injury. Logistic regression was used to examine the association between potential predictors and first return to work by these 3 time-points. RESULTS: Sixty-eight percent of participants returned to work within 6 months. Those who sustained isolated upper extremity injuries were more likely to return to work early. Significant positive determinants of return to work included a strong belief in recovery, the presence of an isolated injury, education to university level and self-employment. Determinants associated with non-return to work included the receipt of compensation, older age, pain attitudes and blue-collar work. The primary reason given for return to work was financial security. CONCLUSION: Demographic, injury, occupation and psychosocial factors were significant predictors of return to work. The relative importance of factors at different time-points suggests that return to work is a multifactorial process that involves the complex interaction of many factors in a time-dependent manner.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 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".