Understanding the factors contributing to farmer suicide: a meta-synthesis of qualitative research
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,105 | 0,228 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,011 |
| Bibliométrie | 0,025 | 0,020 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».