Innovating for Transformation in First Nations Health Using Community-Based Participatory Research
Bibliographic record
Abstract
Community-based participatory research (CBPR) provides the opportunity to engage communities for sustainable change. We share a journey to transformation in our work with eight Manitoba First Nations seeking to improve the health of their communities and discuss lessons learned. The study used community-based participatory research approach for the conceptualization of the study, data collection, analysis, and knowledge translation. It was accomplished through a variety of methods, including qualitative interviews, administrative health data analyses, surveys, and case studies. Research relationships built on strong ethics and protocols to enhance mutual commitment to support community-driven transformation. Collaborative and respectful relationships are platforms for defining and strengthening community health care priorities. We further discuss how partnerships were forged to own and sustain innovations. This article contributes a blueprint for respectful CBPR. The outcome is a community-owned, widely recognized process that is sustainable while fulfilling researcher and funding obligations.
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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 | 0.106 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.015 |
| 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".