Indigenous Health Research in Quebec: Changing the Landscape Through Relationship Building
Bibliographic record
Abstract
As a response to the need for more supportive research environments for Indigenous health research, CIHR created the Network Environments for Indigenous Health Research (NEIHR). The Tahatikonhsontóntie’ Québec Network Environment for Indigenous Health Research (QcNEIHR) is driven by, and grounded in, Indigenous communities in Quebec. This manuscript aims to provide a reflexive account of the QcNEIHR. Using implementation analysis as a methodology, the QcNEIHR evaluator used documents, participant observations and interviews to compare the proposed QcNEIHR grant plan to what the QcNEIHR actualized. The co-authors, members of the Operations Circle, provided additional information and interpretations as the manuscript was being written. The QcNEIHR governance circles were invited to approve the presentation of results. Through this analysis we found that QcNEIHR activities aligned with three of four objectives in the initial research proposal. The Operations Circle of the QcNEIHR successfully navigated through several competing interests, such as, 1) finding a balance between consulting and taking concrete actions, 2) being inclusive while prioritizing a few targeted activities, 3) administering institutional research funds within an Indigenous community-based organization, 4) maintaining an efficient bilingual governance structure with diverse conceptualizations of health and research, 5) managing an organic Operations Circle for innovation and creativity, while assuring an accountability and timely deliverables. During the first four years of operation, the strategy of the QcNEIHR OC was based upon building relationships and mobilizing a diverse lively network. This strategy sets the foundation for community-shared ownership and leadership for the next iteration of the QcNEIHR, where community-driven Indigenous health research in Quebec will continue to strengthen and grow, with the support of provincial and national research institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".