Risk factors for work related injury among male farmers
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for serious farm work related injury among men. METHODS: A case-control study was conducted in Victoria, Australia. Eligible cases (n = 252) were males aged > or =16 years injured while working on a farm and scoring 2 or higher on the Abbreviated Injury Scale. Non-fatal injury cases were identified on presentation to hospital. Fatal cases (next of kin) were recruited via the Coroner's Office. Two age-matched controls per case were recruited by telephone. Data were collected with a structured telephone questionnaire. Logistic regression was used to compare risk factors between cases and controls, adjusting for design factors and average weekly hours worked. RESULTS: The most common external causes of injury were machinery (26%), falls (19%), transport (18%), animals (17%) and being struck by an object (11%). Increased injury risk was observed for being an employee/contractor (odds ratio 1.8, 95% CI 1.2 to 2.7), not having attended farm training courses (1.5, 95% CI 1.0 to 2.1), absence of roll-over protective structures on all/almost all tractors (2.5, 95% CI 1.7 to 3.8), absence of personal protective equipment for chemical use (4.7, 95% CI 1.6 to 13.9) and a low average annual farm income of AUD$5000 or less (2.7, 95% CI 1.3 to 5.6). Decreased injury risk was observed for several health related characteristics and some farm characteristics. CONCLUSION: We identified some risk factors possibly relevant to farm injury prevention programs. However, other factors were not associated with farm work injury suggesting these may not be as important as previously hypothesised.
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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 | 0.001 |
| 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) | 0.002 | 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".