Assault predicts time away from work after claims for work-related mild traumatic brain injury
Bibliographic record
Abstract
INTRODUCTION: Workplace violence carries a substantial economic loss burden. Up to 10% of all traumatic brain injury (TBI) admissions result from physical assault. There remains a paucity of research on assault as a mechanism of injury, taking into account sex, and its association with work re-entry. OBJECTIVES: The aim of this study was to characterise, by sex, the sample of workers who had sustained a work-related mild TBI (wr-mTBI) and to assess the independent influence of assault, as a mechanism of injury, on time away from work. METHODS: A population-based retrospective cohort of workers' compensation claimants in Australia (n=3129) who had sustained a wr-mTBI was used for this study. A multivariable logistic regression analysis assessed whether workers who had sustained wr-mTBI as a result of assault (wr-mTBI-assault) were more likely to claim time off work compared with workers who had sustained a wr-mTBI due to other mechanisms. RESULTS: Among claimants who sustained a wr-mTBI, 9% were as a result of assault. The distribution of demographic and vocational variables differed between the wr-mTBI-assault, and not due to assault, both in the full sample, and separately for men and women. After controlling for potential confounding factors, workers who sustained wr-mTBI-assault, compared with other mechanisms, were more likely to take days off work (OR 2.14, 95% CI 1.53 to 2.99) within a 3-month timeframe. CONCLUSION: The results have policy-related implications. Sex-specific and workplace-specific prevention strategies need to be considered and provisions to support return-to-work and well-being within this vulnerable cohort should be examined.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".