Mental Health and Substance Use of Farmers in Canada during COVID-19
Bibliographic record
Abstract
Farmers in Canada faced higher levels of mental distress than the general public prior to the Coronavirus Disease 2019 (COVID-19) pandemic and are generally less likely than the public to seek help. However, the mental health impacts of COVID-19 on farmers in Canada remain unexplored. Our objective was to investigate mental health outcomes among farmers in Canada by gender and within the context of COVID-19. We conducted a national, online, cross-sectional survey of farmers in Canada (February–May 2021). The survey included validated scales of anxiety, depression, perceived stress, burnout (emotional exhaustion, cynicism, professional efficacy), alcohol use, resilience, and questions regarding participants’ perceived changes in these outcomes during the pandemic. Data were also collected on the impact of COVID-19 specific social and economic factors on mental health, help-seeking, and sense of community belonging through the pandemic. Descriptive statistics were summarized, and Chi-square analyses and t-tests were conducted to compare survey results between genders and to data collected in our similar 2016 survey and normative population data. A total of 1167 farmers participated in the survey. Participants scored more severely across scales than scale norms and the general Canadian population during COVID-19. Scale means were consistent between the 2016 and 2021 samples. Most participants with moderate to severe scores for any outcome reported worsening symptoms since the pandemic began. Women fared significantly worse than men across measures. Over twice as many women reported seeking mental health or substance use support during the pandemic than men. Participants rated the mental health impacts of all social and economic factors related to COVID-19 examined significantly (p < 0.05) differently than the Canadian public. The pandemic has negatively impacted the mental health of farmers in Canada and in ways that differ from the general population. National level and gender-specific mental health supports are needed to help improve the mental health of farmers in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".