Farming and Mental Health Problems and Mental Illness
Bibliographic record
Abstract
BACKGROUND: Farmers experience one of the highest rates of suicide of any industry and there is growing evidence that those involved in farming are at higher risk of developing mental health problems. This article provides an overview of the literature examining mental health issues experienced by farming populations in the United Kingdom, Europe, Australia, Canada and the United States and identifies areas for further research. METHOD: A literature review (Medline, Science Direct, Ingenta, Proquest and PsychINFO) was carried out using the words 'farmers', 'agriculture', 'depression', 'mental health', 'mental illness', 'stress', and 'suicide', as well as a review of relevant papers and publications known to the authors. (Papers not written in English and those published prior to 1985 were excluded.) RESULTS: Fifty-two papers were identified with the majority focusing on stress and coping styles in farmers (24). A number of studies also focused on neuropsychological functioning and agricultural chemical use (7), depression (7), suicide (9), general mental health (4) and injury and mental health (1). This body of research studied male farmers, female farmers, farm workers, farming families, and young people living on farms. Research to date indicates that farmers, farm workers and their respective families face an array of stressors related to the physical environment, structure of farming families and the economic difficulties and uncertainties associated with farming which may be detrimental to their mental health. CONCLUSION: Whilst suicide rates in some groups of farmers are higher than the general population, conclusive data do not exist to indicate whether farmers and farming families experience higher rates of mental health problems compared with the non-farming community. It is clear, however, that farming is associated with a unique set of characteristics that is potentially hazardous to mental health and requires further research.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".