Stress, anxiety, depression, and resilience in Canadian farmers
Bibliographic record
Abstract
PURPOSE: To estimate the prevalence of stress, anxiety, depression, and resilience amongst Canadian farmers. METHODS: An online cross-sectional survey using validated psychometric scales [Perceived Stress Scale (PSS), Hospital Anxiety and Depression Scale, Connor-Davidson Resilience Scale] conducted with farmers in Canada between September 2015 and February 2016. RESULTS: 1132 farmers participated in the study. The average PSS score was 18.9. Approximately 57% and 33% of participants were classified as possible and probable cases for anxiety, respectively; the respective proportions for depression were 34% and 15%. The average resilience score was 71.1. Scores for stress, anxiety, and depression were higher, and resilience lower, than reported normative data. Females scored less favorably on all mental health outcomes studied, highlighting important gender disparities. CONCLUSIONS: These results highlight a significant public health concern amongst farmers, and illustrate a critical need for research and interventions related to farmer mental health. These findings are important for policymakers, physicians, and public and mental health service providers, and can help to inform decision-making, policy recommendations, resource allocation, and development and delivery of training programs for farmers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".