The impact of social, economic and environmental determinants on farmers’ mental health – an international perspective
Notice bibliographique
Résumé
This short version of a systematic literature review aims to provide an overview of research relevant to farmers’ psychosocial work environment and mental health. It contains knowledge about challenges faced by farmers, their consequences in the forms of stress and risk of mental illness, and the capacity to deal with these challenges themselves or through various forms of support. Important aspects also include the occupation’s health factors and opportunities for development that contribute to a good working environment. The research was limited to the period 2005–2021 and to countries that have similar production forms and conditions, including in Europe, North America (United States and Canada) and Australasia (Australia and New Zealand). The results show that the health and safety risks identified in farmers’ psychosocial work environment are workload, finances, climate change and weather conditions, crime, globalisation, laws and regulations, masculine norms and loneliness, isolation, and a lack of support. Issues involving poor mental health are generally more prevalent among farmers, especially older farmers, than in other occupational groups. Farmers have a higher incidence of depression and suicide attempts than other occupational groups, and mental illness among farmers has increased in recent years. Health factors in the psychosocial work environment of farmers are not as well studied as risk factors, with the identified health factors being: the bond felt by the farmer to the cultivated land, environmental and social responsibility, the ability to work, be outside, work physically and eat well, a good working and living environment, working with animals, a reasonable workload, self-motivation, social support and a sense of belonging, an income other than that from working on the farm, and the ability to work after the retirement age. Farmers’ ability to withstand and recover from the stress they face in their occupational role (resilience) varied between individuals. Support from family, nature and animals, and setting limits to work commitments, relaxing, or doing activities other than working also contributed to strengthening their resilience. Resilience is something that can be learned, which can be helpful for farmers. Farmers use different personal strategies to manage the stress they are exposed to (coping), and different coping strategies can also contribute to building farmers’ resilience, which can involve planning, positive reappraisal (change in attitude to stressful events, humour and leisure) and getting help and support from others. Furthermore, acceptance can be used as a coping strategy. Negative strategies can involve avoidance, as well as blaming oneself or others. which may also involve suppressing emotions, avoiding problems, or consuming alcohol. According to several studies, the fact that farmers seem to be less likely to search for and make use of resources and mental health services is due to a lack of regional resources and occupation-specific understanding of the target group. Farmers had the greatest confidence in, and were therefore most receptive to, information about mental health from doctors, as well as from their spouses/family members and friends. The wider agricultural community can contribute to social support, education and mentoring programmes for farmers with symptoms of stress and depression. Future suicide prevention efforts for farmers can also be carried out through education, training programmes and national campaigns.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| 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 ».