A Feasibility Trial of Mental Health First Aid First Nations: Acceptability, Cultural Adaptation, and Preliminary Outcomes
Bibliographic record
Abstract
The Mental Health First Aid First Nations course was adapted from Mental Health First Aid Basic to create a community-based, culturally safe and relevant approach to promoting mental health literacy in First Nations contexts. Over 2.5 days, the course aims to build community capacity by teaching individuals to recognize and respond to mental health crises. This feasibility trial utilized mixed methods to evaluate the acceptability, cultural adaptation, and preliminary effectiveness of MHFAFN. Our approach was grounded in community-based participatory research principles, emphasizing relationship-driven procedures to collecting data and choice for how participants shared their voices. Data included participant interviews (n = 89), and surveys (n = 91) from 10 groups in four provinces. Surveys contained open-ended questions, retrospective pre-post ratings, and a scenario. We utilized data from nine facilitator interviews and 24 facilitator implementation surveys. The different lines of evidence converged to highlight strong acceptability, mixed reactions to the cultural adaptation, and gains in participants' knowledge, mental health first aid skill application, awareness, and self-efficacy, and reductions in stigma beliefs. Beyond promoting individual gains, the course served as a community-wide prevention approach by situating mental health in a colonial context and highlighting local resources and cultural strengths for promoting mental well-being.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".