What Impacts Perceived Stress among Canadian Farmers? A Mixed-Methods Analysis
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Bibliographic record
Abstract
Globally, farmers report high levels of occupational stress. The purpose of this study was to identify and explore factors associated with perceived stress among Canadian farmers. A sequential explanatory mixed-methods design was used. An online cross-sectional national survey of Canadian farmers (n = 1132) was conducted in 2015–2016 to collect data on mental health, demographic, lifestyle, and farming characteristics; stress was measured using the Perceived Stress Scale. A multivariable linear regression model was used to investigate the factors associated with perceived stress score. Qualitative interviews (n = 75) were conducted in 2017–2018 with farmers and agricultural sector workers in Ontario, Canada, to explore the lived experience of stress. The qualitative interview data were analyzed via thematic analysis and then used to explain and provide depth to the quantitative results. Financial stress (highest category—a lot: (B = 2.30; CI: 1.59, 3.00)), woman gender (B = 0.55; CI: 0.12, 0.99), pig farming (B = 1.07; CI: 0.45, 1.69), and perceived lack of support from family (B = 1.18; CI: 0.39, 1.98) and industry (B = 1.15; CI: 0.16–2.14) were positively associated with higher perceived stress scores, as were depression and anxiety (as part of an interaction). Resilience had a small negative association with perceived stress (B = −0.04; CI: −0.06, −0.03). Results from the qualitative analysis showed that the uncertainty around financial stress increased perceived stress. Women farmers described the unique demands and challenges they face that contributed to their overall stress. Results from this study can inform the development of mental health resources and research aimed at decreasing stress among Canadian farmers.
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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 | 0.002 | 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) | 0.001 | 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 it