Understanding the factors contributing to farmer suicide: a meta-synthesis of qualitative research
Bibliographic record
Abstract
INTRODUCTION: Farming is associated with a range of ongoing occupational stressors that place farmers at an elevated risk for suicide. The increase of farmer suicide in recent years represents an important public health concern and requires an understanding of the circumstances and risk factors that contributed to a farmer's decision to die by suicide, as well as the protective factors that can help farmers manage the stressors. Qualitative research examining farmer suicide has grown in recent years and provides a rich description of the farmers' lives leading up to their suicide that cannot be easily captured from quantitative surveys. Therefore, we conducted a systematic review and meta-synthesis to understand the risk and protective factors preceding the farmers' suicide from the perspectives of their partner, relatives, or individuals who worked closely with them. We used this information to generate a conceptual model to illustrate the intersecting nature of farm culture, work-life stressors and mental health. METHODS: We conducted a comprehensive literature search for peer-reviewed studies using electronic databases Embase, PsycINFO, Academic Search Complete, PubMed and Scopus using a combination of search terms related to farming and suicide. All searching was conducted by two independent researchers. The selected studies were critically appraised using standardized forms to assess study quality. The qualitative data from each study was analyzed using meta-ethnography to identify underlying themes related to suicide and new interpretations of the topic while retaining the original meaning of each qualitative study. RESULTS: After independently screening studies, our final sample included 14 studies. We identified seven themes that contributed to farmer suicide: maintaining a 'farmer' identity, financial crisis, support and stress of family, the community panopticon, isolation from others, access to toxins and firearms, and an unpredictable environment. Using these themes, we developed a conceptual model called the Farming Adversity-Resilience Management framework (ie FARM framework) to highlight the cyclical and dynamic pattern of farm culture and to illustrate the risk factors that contribute to vulnerability to poor mental health and even suicide. This model also identifies a variety of protective factors that can improve farmers' resilience to such stressors. CONCLUSION: This is the first study to synthesize qualitative data about farmer suicide. While the enduring challenges and stressors of farming in rural areas may never be eliminated, there may be ways to help farmers build resilience to these factors. Our FARM framework presents a new way of understanding farm culture, the occupational stressors and farmers' wellbeing while also providing direction for future research and guidance for practical interventions. Policymakers and healthcare providers should consider developing and delivering mental health literacy programs to farmers and those who work closely with them to identify symptoms of poor mental health and to facilitate attitude change. Greater access to health care should be a priority in rural areas, and clinicians should be familiar with the stressors farmers face so that they can ask questions about their work-life balance to better assess the farmer's mental health and risk of suicide.
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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 | 0.105 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".