Integrating Indigenous Ways of Knowing Into Learning Health Systems: Moving From Learning Health Systems to Learning Communities
Bibliographic record
Abstract
In Canada, various health organizations, research bodies and funders at the federal and provincial levels have been supporting learning health system (LHS) initiatives, including in youth mental health.1,2 LHS are health networks that continuously self-study, adapt and improve health services by leveraging routinely collected clinical data and research data and engaging partners in identifying shared problems, co-designing solutions, and accelerating changes in practices and policy.3,4 LHS continuously cycle from practice to data (services generate data for learning); data to knowledge (data is analyzed to generate insights); and knowledge to practice (knowledge is applied to enhance practice and policy). An important gap in the literature and in practice is the integration of Indigenous wisdom and ways of knowing in the LHS approach, which we begin addressing in this article. The authors are an Elder, a national Indigenous organization leader, and Indigenous and non-Indigenous researchers and clinician-scientists, who are part of the ACCESS Open Minds Indigenous Youth Mental Health and Wellness Network (a Canadian Institute of Health Research-funded national LHS network). [...]
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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 | 0.030 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.079 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".