Prevention of agricultural injuries: an evaluation of an education-based intervention
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
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.
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 | 0.001 | 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 | 0.001 |
| 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