Work-related mild-moderate traumatic brain injury and the construction industry
Bibliographic record
Abstract
BACKGROUND: Consequences of traumatic brain injury underscore the need to study high-risk groups. Few studies have investigated work-related traumatic brain injuries (WrTBIs) in the construction industry. OBJECTIVE: To examine WrTBIs in Ontario for the construction industry compared to other industries. METHODS: A retrospective study of individuals who sustained a WrTBI and had a clinical assessment as an outpatient at a hospital-based referral centre. Data were collected for a number of factors including demographic, injury and occupation and were analyzed according to the Person-Environment-Occupation (PEO) model. PARTICIPANTS: 435 individuals who sustained a WrTBI. RESULTS: There were 19.1% in the construction industry, 80.9% in other industries. Compared to other industries, individuals in the construction industry were more likely to be male, to not have attained post-secondary education, and experience multiple traumas. WrTBIs in the construction industry were commonly due to elevated work. The construction occupations involved included skilled workers and general labourers, and compared to other industries, WrTBIs occurred most often for those employed for a short duration in the construction industry. CONCLUSIONS: Construction industry workers experience serious WrTBIs that are amenable to prevention. Use of the PEO model increased our understanding of WrTBIs in the construction industry.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".