Determinants of agricultural injury: a novel application of population health theory
Bibliographic record
Abstract
OBJECTIVES: (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 (2007-09). 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: Individual exposure: self-reported times involved in farm work. Contextual factors: scaled measures describe socioeconomic, physical, and cultural farm environments. OUTCOME: time to first self-reported farm injury. RESULTS: 450 farm injuries were reported for 370 individuals on 338 farms over 2 years of follow-up. Times involved in farm work were strongly and consistently related to time to first injury event, with strong monotonic increases in risk observed between none, part-time, and full-time work hour categories. Relationships between farm work hours and time to first injury were not modified by the contextual factors. Respondents reporting high versus low levels of physical farm hazards at baseline experienced increased risks for farm injury on follow-up (HR 1.54; 95% CI 1.16 to 1.47). CONCLUSIONS: Based on study findings, firm conclusions cannot be drawn about the application of population health theory to the study of farm injury aetiology. Injury prevention efforts should continue to focus on: (1) sound occupational health and safety practices associated with long work hours; (2) physical risks and hazards on farms.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".