Older farmers and machinery exposure—cause for concern?
Bibliographic record
Abstract
BACKGROUND: The average age of farmers in North America is increasing each year. We had the unique opportunity to examine work patterns and how they change across the lifespan in a large cohort of farm operations. METHODS: Saskatchewan farms were surveyed via questionnaire during the winter of 2007 to examine the determinants of injury. A sub-sample of 2,751 male farmers aged 25 and older was used in this project. The primary dependent variable was the proportion of work time devoted to specific farm tasks which was related to advancing age. RESULTS: The weekly hours of work declined approximately 34% as farmers aged over the lifespan. Older farmers disproportionately retained tasks involving tractors and combines as they aged, so that the proportion of time spent operating machinery such as tractors and combines increased by about 40% in the older age groups. CONCLUSION: Exposure to potentially dangerous farm equipment does not decrease as much as would be expected based on an equal linear reduction in all work tasks as overall work quantity decreases with age. Older farmers remain relatively active in the workplace, and, therefore, prevention efforts should focus on safe machinery operation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".