Tailored Mental Health Literacy Training Improves Mental Health Knowledge and Confidence among Canadian Farmers
Bibliographic record
Abstract
We hypothesized that "In the Know" would significantly increase participants' knowledge around mental health, confidence in recognizing mental health struggles, confidence in speaking about mental health with others, and confidence in helping someone who may be struggling with mental health. "In the Know" was a 4-h, in-person program delivered by a mental health professional who also had experience in agriculture. Six sessions were offered in Ontario, Canada in 2018. Participants were farmers and/or worked primarily with farmers. A pre-training paper questionnaire was administered, followed by a post-training questionnaire at the end of the session and 3 and 6 month post-training questionnaires via email. Wilcoxon signed-rank tests were performed to compare participants' self-reported knowledge and confidence across four timepoints. "In the Know" significantly improved participants' self-reported mental health knowledge and confidence in recognizing mental health struggles, speaking to others, and helping others who are struggling immediately following training and often at 3 and 6 months post-training. This is the first study among farming populations to measure program impact with 3- and 6-month follow-ups. Given the reported associations between mental health literacy and increased help-seeking, disseminating "In the Know" more broadly across farming communities may help to increase mental health literacy and thus increase help-seeking among 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".