Centering Indigenous Knowledges and Worldviews: Applying the Indigenist Ecological Systems Model to Youth Mental Health and Wellness Research and Programs
Bibliographic record
Abstract
Globally, Indigenous communities, leaders, mental health providers, and scholars have called for strengths-based approaches to mental health that align with Indigenous and holistic concepts of health and wellness. We applied the Indigenist Ecological Systems Model to strengths-based case examples of Indigenous youth mental health and wellness work occurring in CANZUS (Canada, Australia, New Zealand, and United States). The case examples include research, community-led programs, and national advocacy. Indigenous youth development and well-being occur through strengths-based relationships across interconnected environmental levels. This approach promotes Indigenous youth and communities considering complete ecologies of Indigenous youth to foster their whole health, including mental health. Future research and programming will benefit from understanding and identifying common, strengths-based solutions beyond narrow intervention targets. This approach not only promotes Indigenous youth health and mental health, but ripples out across the entire ecosystem to promote community well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".