Tractors, Talk, Mindset, Mantras, Detachment, and Distraction: A Mixed-Methods Investigation of Coping Strategies Used by Farmers in Canada
Bibliographic record
Abstract
Characterized by high unpredictability and little control, everyday factors make farming one of the most stressful occupations globally. Indeed, farmers around the world and in Canada score more severely on measures of perceived stress and negative mental health outcomes like anxiety and depression, and suicide ideation among farmers is disproportionately high. Research investigating effective ways of coping with everyday stress within the time and workload constraints of farming is scarce. This mixed-methods study explores the ways farmers in Ontario and Canada cope with daily farming stressors. Qualitative data from 75 in-depth interviews with farmers and industry professionals in Ontario, Canada, were analyzed to investigate farming-specific coping strategies within the farming context. Quantitative survey responses from 1167 farmers across Canada to the 14-item Ways of Coping measure developed for the Canadian Community Health Survey Cycle 1.2 were analyzed to determine which coping strategies Canadian farmers use most in relation to the representative national population. The ways of coping endorsed by farmers are presented in this paper, including adaptations of positive coping strategies in the farming context. The descriptions of positive and negative coping strategies used provide direction for effective avenues to reduce stress and boost farmers’ well-being.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".