Prevention of agricultural injuries: an evaluation of an education-based intervention
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of an agricultural health and safety program in reducing risks of injury. DESIGN: Cross-sectional survey. SETTING: 50 rural municipalities in the Province of Saskatchewan, Canada. INTERVENTION: The Agricultural Health and Safety Network (AHSN), a mainly educational program that administered 112 farm safety interventions over 19 years. SUBJECTS: 5292 farm people associated with 2392 Saskatchewan farms. Farms and associated farm people were categorized into three groups according to years of participation in the AHSN. IMPACT: self-reported prevalence of: (1) farm safety practices; (2) physical farm hazards. OUTCOME: (1) self-reported agricultural injuries. RESULTS: After adjustment for group imbalances and clustering at the rural municipality level, the prevalence of all impact and outcome measures was not significantly different on farms grouped according to years of AHSN participation. To illustrate, the adjusted relative risk of reporting no rollover protection on tractors among farms with none (0 years) versus high (>8 years) levels of AHSN participation was 0.95 (95% CI 0.69 to 1.30). The adjusted relative risk for agricultural injuries (all types) reported for the year before the survey was 0.99 (95% CI 0.74 to 1.32). CONCLUSIONS: Educational interventions delivered via the AHSN program were not associated with observable differences in farm safety practices, physical farm hazards, or farm-related injury outcomes. There is a need for the agricultural sector to extend the scope of its injury prevention initiatives to include the full public health model of education, engineering, and regulation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".