First Nations, Métis, Inuit Youth Mental Health and Indigenous (FNMI) Ways of Knowing: A Theoretical Interpretation and Application
Bibliographic record
Abstract
Due to the colonial genocides that have happened (and continue to happen) in Aotearoa, Australia, Turtle Island and beyond, and Indigenous rights movements that have generated iconic, historical shifts in research praxes to improve the health of Indigenous Peoples, globally. Mental health services for Indigenous youth require an approach and design grounded in Indigenous Ways of Knowing. Through this theoretical paradigm shift, researchers in public health are starting to understand that cultural safety is critical in the delivery of FNMI (Indigenous) health services that actually provide healing and do not further harm people (deliberately or not). Still in progress, public health researchers and youth mental health service providers continue to (allegedly unknowingly) uphold the colonial legacies, highlighting an urgent need for Indigenous Ways of Knowing and Doing. As we discuss Indigenous Ways of Knowing and Doing, the reader will find descriptions of the main theoretical tenets with examples of their application. Strengths and challenges will serve as the throughline for the discussion. The paper concludes with the extension of these tenets and their potential application for mental health services for Indigenous youth.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".