Anishinabek sources of strength: Learning from First Nations people who have experienced mental health and substance use challenges
Bibliographic record
Abstract
We report on the system of care and sources of strength and resilience for mental health among First Nations People experiencing the impacts of historical and contemporary colonization. Aamjiwnaang First Nation, a vibrant community of approximately 2400 members in southwestern Ontario, Canada, partnered in research to reveal sources of strength and resilience among community members with lived experiences (PWLE) with mental health and/or substance use challenges. A thematic content analysis was done using qualitative data collected as part of two complementary studies. In the first study called the "Five Views on a Journey" study, interviews with PWLE and family members of PWLE were conducted to better understand strengths and deficits in the system of care for mental health and substance use. In the second study entitled "A Strengths-Based Approach to Understanding How First Nations People Cope with Stress and Trauma," Photovoice was used to examine sources of strength and resilience among PWLE. Combined, these studies revealed that mental health supports and services that are trustworthy, open, and confidential are foundational to healing, helping PWLE find pathways to wellness by engendering feelings of hope, self-worth and pride. The integral roles of Anishinaabe culture and cultural identity as well as strong connections with family and community were key sources of strength and resilience. Our findings are discussed in the context of Aamjiwnaang's informal and formal systems of care, culture as wellness, inner strength, and the Truth and Reconciliation Commission of Canada's Calls to Action.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".