The RIPPLES of Meaningful Involvement: A Framework for Meaningfully Involving Indigenous Peoples in Health Policy Decision-Making
Bibliographic record
Abstract
Indigenous Peoples experience the greatest health inequities in Canada and other colonized countries, yet are routinely excluded from health-related policy decisions. Those advocating for Indigenous health equity are often left wrestling with the question: What constitutes, and what can foster, meaningful involvement of Indigenous Peoples in the contemporary health policy climate? Twenty (n = 20) in-depth, open-ended interviews with Indigenous and non-Indigenous leaders in health and health policy were conducted with a view to understanding what constitutes meaningful involvement of Indigenous Peoples in health policy decision-making. The analysis suggests meaningful involvement requires attuning to underlying power dynamics inherent in policy making and taking action to decolonize and transform the policy system itself. Based on these findings, the authors offer a framework for meaningful involvement.
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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 | 0.068 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.030 | 0.148 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".