Determinants of agricultural injury: a novel application of population health theory
Bibliographic record
Abstract
Objective (1) To apply novel population health theory to the modelling of injury experiences in one particular research context, (2) To enhance understanding of the conditions and practices that lead to farm injury. Design Prospective cohort study conducted over 2 years (2007–2009). Setting 50 rural municipalities in the Province of Saskatchewan, Canada. Subjects 5038 participants from 2169 Saskatchewan farms, contributing 10 092 person-years of follow-up. Main Measures Exposure. Self-reported times involved in farm work; Effect Modifiers. Scaled measures describe socio-economic, physical and cultural farm environments; Outcome: Self-reported farm injuries. Results 450 farm injuries were reported for 370 individuals on 338 farms over 2 years of follow-up. Amounts of farm work exposure were strongly and consistently related to time to first injury event. Relationships between farm work hours and time to first injury were not modified in the directions suggested by theory between levels of the socio-economic, physical and cultural farm work environments. Respondents reporting high versus low levels of physical farm hazards at baseline experienced elevated risks for farm injury upon follow-up (Hazard Ratio 1.54; 95% CI 1.16 to 1.47). Conclusions Study findings failed to show interactions consistent with population health theory. Injury prevention efforts should continue to focus on: (1) sound occupational safety practices associated with long work hours; (2) physical risks and hazards on farms and (3) more speculatively, behavioural modification to minimise occupational injury risks.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".