“An Incredible Amount of Stress before You Even Put a Shovel in the Ground”: A Mixed Methods Analysis of Farming Stressors in Canada
Bibliographic record
Abstract
Farming is widely regarded as a highly stressful occupation, and many farming stressors have been studied globally. Research on farming stressors in Canada is scarce, yet there is some indication that Canadian farmers have high perceived stress scores and score more severely across mental health outcomes compared to the general population. This study provides a comprehensive exploration of farming stressors in Canada with the aim to inform avenues to reduce stress and/or boost the well-being of farmers. An exploratory sequential mixed-methods design was used. First, qualitative data were collected from 75 in-depth interviews with farmers and industry professionals from Ontario, Canada from 2017 to 2018. These data were then used to inform items measuring self-reported stress across 12 farming stressors in a national cross-sectional survey of farmers’ mental health conducted February–May 2021. Results from both data sources provide an initial understanding of the episodic and chronic stressors faced by farmers in Canada, and the context within which these stressors are experienced. Implications and focus areas for stress reduction and well-being promotion are discussed in this paper.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".