A community-driven and evidence-based approach to developing mental wellness strategies in First Nations: a program protocol
Bibliographic record
Abstract
BACKGROUND: Mental health, substance use/addiction and violence (MSV) are important issues affecting the well-being of Indigenous People in Canada. This paper outlines the protocol for a research-to-action program called the Mental Wellness Program (MWP). The MWP aims to increase community capacity, promote relationship-building among communities, and close gaps in services through processes that place value on and supports Indigenous communities' rights to self-determination and control. The MWP involves collecting and using local data to develop and implement community-specific mental wellness strategies in five First Nations in Ontario. METHODS: The MWP has four key phases. Phase 1 (data collection) includes a community-wide survey to understand MSV issues, service needs and community strengths; in-depth interviews with individuals with lived experiences with MSV issues to understand, health system strengths, service gaps and challenges, as well as individual and community resilience factors; and focus groups with service providers to improve understanding of system weaknesses and strengths in addressing MSV. Phase 2 (review and synthesis) involves analysis of results from these local data sources and knowledge-sharing events to identify a priority area for strategic development based on local strengths and need. Phase 3 (participatory action research approach) involves community members, including persons with lived experience, working with the community and local service providers to develop, implement, and evaluate the MWP to address the selected priority area. Phase 4 (share) is focused on developing and implementing effective knowledge-sharing initiatives. Guidelines and models for building the MWP are shared regionally and provincially through forums, webinars, and social media, as well as cross-community mentoring. DISCUSSION: First Nations people that can be used by other First Nations to identify shared wellness priorities in each community, and determine and execute next steps in addressing areas of main concern.
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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 | 0.162 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.080 | 0.013 |
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".