Work-related traumatic brain injury: A brief report on workers perspective on job and health and safety training, supervision, and injury preventability
Bibliographic record
Abstract
BACKGROUND: Although work-related injuries are on the decline, rates of work-related traumatic brain injury (wrTBI) continue to rise. As even mild wrTBI can result in cognitive, behavioural, and functional impairments that can last for months and even years, injury prevention is a primary research focus. Administrative claims data have provided valuable insights into the mechanisms that cause wrTBI; however, data from the perspective of injured workers on wrTBI prevention is limited. OBJECTIVE: Our study aimed to better understand the factors that precipitate wrTBI, as perceived by injured workers. METHODS: We recruited 101 injured workers from a neurology services clinic with a province-wide catchment area in a large, urban teaching hospital and studied perceived preventability of these injuries from the injured workers' perspective. RESULTS: Key findings were that nearly 80% of injuries were perceived as preventable, and nearly 25% and 50% of workers reported that they did not receive job and health and safety training, respectively. Less than half of all workers reported being regularly supervised, and of those who were supervised, approximately two-thirds reported that supervision was adequate. Moreover, 84% and 77% reported they were advised to rest and take time-off after the injury, respectively. CONCLUSIONS: Our study is the first to show that the vast majority of injured workers consider their wrTBI to be preventable. In addition, we found that training and supervision are two areas that can be targeted by wrTBI prevention strategies. Our study provides valuable and unique perspectives to consider when designing wrTBI prevention initiatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".