Physical and psychological determinants of injury in Ontario forest firefighters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Forest firefighters are faced with multiple physical and psychological challenges as a result of their duties. Little is known about the determinants of injury among these workers. The Ontario Ministry of Natural Resources (OMNR) Aviation, Forest Fire and Emergency Services (AFFES) records detailed information on two mutually exclusive types of workplace injury: First aid (self-reported) and Workplace Safety Insurance Board (WSIB, i.e. received medical attention). AIMS: To identify the contributions of physical and psychological factors on the likelihood of injury among forest firefighters. METHODS: Participants were male and female forest firefighters aged between 18 and 65. Data were collected using two self-administered instruments: The NEO Personality Inventory and the Job Stress Survey. Secondary data were collected from the OMNR AFFES and data were analysed by way of multivariate statistical procedures. RESULTS: There were 252 participants. Those who were older, had a history of injury, had high scores for the personality construct of Neuroticism or low scores for the Openness construct were significantly more likely to incur a first aid injury, while those with high experience levels were significantly less likely to incur injury (P < 0.05). High job stress was the only significant predictor of WSIB injury (P < 0.05). CONCLUSIONS: First aid and WSIB injuries in the OMNR AFFES were quite distinct phenomena and different factors need consideration in their prediction. It is recommended that managers and decision-makers in this field consider factors such as job stress, personality and the prior occurrence of injuries in their assessment of risk.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it