The impact of social, economic and environmental determinants on farmers’ mental health – an international perspective
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".