Utility of Person–Environment–Occupation model in exploring sex-specific causes of work-related traumatic brain injury: a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Work-related traumatic brain injury (wr-TBI) is on the rise. The pre-injury period, a significant consideration for preventive initiatives, is largely unexplored. OBJECTIVES: To identify Person-Environment-Occupation (PEO) variables associated with wr-TBI to inform sex-specific primary prevention. METHODS: -test and chi-squared tests were used to study sex differences. Multivariate logistic regression models of wr-TBI were fit with a priori defined PEO variables. RESULTS: The sample comprised 330 consecutive workers with wr-TBI (40.8 ± 11.1 years old, 71% male). Sex differences were observed across PEO variables. In multivariable logistic regression analyses the odds of sustaining a wr-TBI from a fall increased with the presence of a mood disorder and participation in non-labourer occupations (odds ratio (OR) 2.89 (95% CI 1.06-7.89) and OR 2.89 (95% CI 1.06-7.89), respectively) and decreased being a male (OR 0.31 (95% CI 0.17-0.54)). The odds of sustaining a wr-TBI from being striken by an object was greater in workers with prior head injury (OR 2.8 (95% CI 1.24-6.45)). None of the variables studied were associated with wr-TBI sustained from being striken against an object. CONCLUSIONS: Workers' health status pre-injury is associated with external causes of wr-TBI. Sex differences across PEO categories warrant further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".