Research, Sovereignty and Action: Lessons from a First Nations-Led Study on Aging in Ontario
Bibliographic record
Abstract
First Nations in Ontario are building capacity to leverage health services data in Ontario to provide robust, First Nations-driven health evidence.Beyond providing evidence, population health research processes must involve diverse First Nations' perspectives, collective capacity building and translation of research findings into action through integrated and community engaged knowledge translation and exchange (KTE) approaches.Suggested ways include integrating stories and traditional knowledge, prioritizing gatherings and establishing an enduring commitment to action.To effectively support First Nations' self-determination and sovereignty, First Nations' principles of ownership, control, access and possession (OCAP ® ) in research could be expanded to include "action" (OCAPA). Key Points• While vital to the realization of data sovereignty and the generation of First Nations-centred knowledge, research that is OCAP ® -aligned does not necessarily lead to community action and uptake.• It is important to actively share findings from First Nations health research in ways that align with communities' preferred formats, venues and information sources.• There is a need to reframe conversations around knowledge translation and exchange (KTE) for First Nations health research.Effective KTE should support self-determination and sovereignty.
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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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".