Stress on the Farm and Its Association with Injury
Bibliographic record
Abstract
The objectives of this study were to examine associations between perceived psychosocial stress and farm injury among men and women in Ontario, Canada. Cross-sectional data from the Ontario Farm Family Health Study were used to investigate perceived levels of stress, farm injuries and their interrelationships. Age-standardized rates of injury were 13.3/100/year and 3.8/100/year for men and women, respectively. The most common types of injury were strains/sprains/torn ligaments and cuts/lacerations. Approximately 18% of men and 11% of women reported that their lives were "very stressful." Common sources of stress were money worries and feeling overworked. The risk for farm injury increased with level of stress. For men, the adjusted odds ratios for injury were: 1.00 (referent), 1.02 (95% CI: 0.72, 1.42), and 1.61 (95% CI: 1.08, 2.41)for lowest to highest stress levels, respectively. For women, adjusted odds ratios were: 1.00 (referent), 1.43 (95% CI: 0.83, 2.47), and 2.73 (95% CI: 1.38, 5.39). These risks were especially pronounced among women who were not employed off the farm. This study represents a novel quantitative analysis examining associations between perceived psychological stress and farm injury. Future research should investigate these associations in other farm populations, confirm their temporal directions, and further explore the effect of gender on the strength of these associations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".