Investigating mental health, help-seeking, and tailored mental health programming among Canadian farmers
Bibliographic record
Abstract
Within Canada, there is a paucity of research examining farmer mental health. To begin to address this gap, this thesis aimed to systematically map published research about mental health outcomes and interventions among farming populations worldwide; explore factors associated with perceived stress and help-seeking among Canadian farmers; and evaluate an agriculture-specific mental health literacy program. This research comprised four interrelated studies. A scoping review of mental health outcomes and services among farmers (Chapter 2) reported that stress, suicide, and depression were the most studied mental health outcomes worldwide, and research gaps in Canada were confirmed. Chapter 3 explored factors associated with perceived stress among Canadian farmers using a mixed-methods approach. A multivariable linear regression model, developed using the Producer Stress and Resilience Survey (n=1132), predicted that female gender, financial stress, pig farming, perceived lack of support from family and industry, and an interaction between anxiety and depression were positively associated with higher perceived stress scores. Resilience was negatively associated with perceived stress. Using data from 75 semi-structured interviews, these factors were explored through thematic analysis. Chapter 4 explored help-seeking among Ontario farmers. Thematic analysis resulted in five themes around help-seeking motivations and barriers, including accessibility of mental health supports, stigma in the community, anonymity in seeking support, 'farm credibility', and recommendations for implementing mental health services for the agricultural community. The results of Chapter 3 and 4, along with consultation with an agricultural stakeholder working group, provided the base knowledge for the development of 'In the Know': A mental health literacy training for Canadian agriculture. Chapter 5 evaluated the effectiveness of 'In the Know' over a 6-month period. Results indicated improvements of participants' mental health knowledge, confidence in recognizing and speaking to others about mental health struggles, and confidence in helping others with mental health. Results from this thesis highlighted research gaps around farmer mental health, and explored factors associated with perceived stress and help-seeking. Further, this research emphasized that including farmers' perspectives within investigations around mental health and in the development of supports could lead to more effective services, increased help-seeking, and improved farmer mental health 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".