“Farmers Aren’t into the Emotions and Things, Right?”: A Qualitative Exploration of Motivations and Barriers for Mental Health Help-Seeking among Canadian Farmers
Bibliographic record
Abstract
Working in agriculture has been associated with an increased prevalence of psychological distress and mental health concerns. Farmers are also less likely than non-farmers to seek-help for their mental health. Previous research examining help-seeking among farmers has focused predominantly on male farmers, and has not included many of the Canadian agricultural commodity groups or provinces. The goal of this study was to explore perceptions of farmer help-seeking for mental health amongst farmers and people who work with farmers. The study objectives were to characterize the motivations and barriers to help-seeking behaviours. Semi-structured interviews were conducted with 75 farmers and individuals who work with farmers in Ontario, Canada, between 2017 and 2018. Interviews were conducted in person, and by telephone when needed. Topics of discussion included farming stresses and their impacts; personal well-being; agricultural crises and mental health help-seeking; use of mental health supports; motivators and barriers to help-seeking; and perceived ideals for mental health supports. Thematic analysis was conducted collaboratively by three authors using inductive and deductive coding. Our analysis resulted in five themes around help-seeking motivations and barriers: 1) Accessibility of mental health supports and services; 2) Stigma around mental health in the agricultural community; 3) Anonymity and/or lack of anonymity in seeking support; 4) Farm credibility; and 5) Recommendations for implementing mental health services for the agricultural community. This study provides insights around how farming culture and the accessibility and delivery of services may influence help-seeking for mental health, and proposes strategies to break down barriers to help-seeking in this population.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".