Poor mental health negatively impacts farmers personally, interpersonally, cognitively, and professionally
Bibliographic record
Abstract
Farmers globally face significant occupational stressors and are reported to experience high levels of depression, anxiety, burnout, suicide ideation, and suicide. While the impacts of high stress and poor mental health have been well-studied in the general population, and to some extent, in specific occupations, the impacts on farmers are understudied. The objective here was to explore the lived experience of high stress and (or) poor mental health in Canadian farmers, including the perceived impacts. Using a phenomenological approach within a constructivist paradigm, we conducted 75 one-on-one research interviews with farmers and people who work closely with farmers, in Ontario, Canada, between July 2017 and May 2018. We analysed the data via thematic analyses and identified four major themes. Participants described myriad negative impacts of farmers’ high stress and (or) poor mental health: (1) personally, (2) interpersonally, and (3) cognitively, which ultimately negatively impacted them (4) professionally, including consequences for productivity, animals, and farm success. The data described far-reaching, interconnected impacts of high stress and poor mental health on participants, the people and animals in their lives, and most aspects of their farming operations, financial viability, and success. Farmer stress, mental health, and well-being are important considerations in promoting sustainable, successful agriculture.
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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 | 0.001 | 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 | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".