The Mental Health of Farmers and Farmworkers Impacted by Flooding and Drought: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Farmers and farmworkers in England are a population group who are particularly vulnerable to the mental health impacts of environmental stressors, including flooding and droughts. While international studies, particularly from Australia, have examined these impacts, there is a critical gap in understanding the mental health consequences of such events for England's farming community. This gap is particularly concerning given the increase in frequency and severity of flooding and drought events due to climate change, which will have significant repercussions for the mental well-being of those whose livelihoods depend on affected land. OBJECTIVE: This study aims to investigate the mental health impacts of flooding and drought events on farmers and farmworkers in England. It will examine how these events affect their livelihoods and explore both compounding and mitigating factors associated with mental health challenges. METHODS: A mixed methods approach will be used, beginning with a national online survey distributed via trusted intermediary agricultural organizations from January 2025 to March 2025. The survey will include the Warwick-Edinburgh Mental Wellbeing Scale to assess participants' mental well-being. A subset of respondents will be selected for follow-up semistructured interviews in late 2025 to early 2026 to gather more in-depth data. Data will be thematically analyzed, allowing for the identification of key patterns in mental health impacts, coping mechanisms, and resilience-building strategies. RESULTS: Survey and interview data will be analyzed during 2025 and 2026 to identify themes and patterns related to the mental health challenges faced by the farming community. This will include exploring coping mechanisms, support networks, and resilience-building strategies, with findings used to inform national interventions. Data obtained from the Warwick-Edinburgh Mental Wellbeing Scale component will be assessed against results from existing studies that have used the scale for other population groups to provide comparative results. CONCLUSIONS: Findings from this study will inform the development of health protection interventions, supporting the farming community in building resilience to future flooding and drought events. This study will also provide valuable insights into the mental health impacts of flooding and droughts in England, contributing to the growing international research on the mental health effects of climate change. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73827.
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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 | 0.060 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 0.018 |
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".