Farmers’ health and wellbeing in the context of changing farming practice: a qualitative study
Bibliographic record
Abstract
Abstract Background Farming continues to change globally, with steady industrialization, globalization and climate change and disproportionately high reports of stress and suicide. Little research has been done to understand how changes to farming impact mental health. We aimed to understand how Canadian farmers understand their stressors and experience their health. Methods We recruited 16 small-medium scale, diversified farmers through farm organizations in Grey-Bruce counties in Canada. We interviewed them about their perception of changes in farming, associated stressors, mental health and well-being, and community supports. Using a constructivist paradigm, we coded each interview, discussed results, and formulated emergent themes using thematic analysis. Results Farmers’ relationship to change was complex with both benefits and challenges of changing farm practices, technology and weather for health - a “double-edged sword”. Farmers described the resilience associated with farming which connects them to the land “essentially being at one with place.” Farmers’ work required them to be active, an asset for keeping them healthy, but also a challenge if mobility became restricted. Farmers’ noted overwhelming stress but stated “...the last thing most farmers want to do is admit that they are stressed or have a mental health issue.” Yet “...if you don’t have strong mental health then you can’t really be resilient and cope with the stresses of climate change and all the things that will happen on a farm.” They voiced a perceived lack of support from governments - dealing with bureaucracy, community - experience of isolation or stigma, and health services - an over-stretched, often distant system. Conclusions Farmers’ understandings from Canada will be compared to literature from Europe to demonstrate relevance inform public health programs promoting mental health in rural communities, advocacy for government supports to diversified farmers and evaluation of intervention programs. Key messages Farmers experience change as a double edged sword with benefits and challenges for health and mental health. Public health needs to intervene to meet farmers where they are and to advocate with farmers for further support.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".