Sex differences in work-related traumatic brain injury: a concurrent mixed methods study employing the person-environment-occupation model
Bibliographic record
Abstract
BACKGROUND: Work-related traumatic brain injury (wrTBI) is considered a critical injury that can be prevented. Few studies have integrated clinical data and workers' injury narratives to inform sex-specific wrTBI prevention. OBJECTIVE: To examine sex differences in pre-injury factors and provide recommendations for primary prevention of wrTBI. METHODS: Concurrent mixed methods study. The Person-Environment-Occupation (PEO) model served as a theoretical framework for qualitative and quantitative data analyses. RESULTS: The sample consisted of 93 workers (51% female, 67% aged over 40) with wrTBI sustained as a result of being struck by/against an object (SBA, 46%), falls (30%), motor vehicle accident (13%), and assault (11%). Qualitative analysis of injury events revealed distinct patterns between male and female workers in the nature and physical/social load of occupational activities performed at the time of injury. Quantitative analysis enriched interpretation of observed sex differences across PEO factors. New insights emerged by stratifying SBA injury cases, revealing sex differences in Environment- and Occupation-related factors unique to workers struck by an object. IMPLICATIONS: Sex- and cause-specific analysis of injury events is essential for surveillance and prevention of wrTBI. Addressing fitness for duty, supervisor-worker relationships, and industry-specific hazards in prevention strategies is essential to ensure workplace safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".