Learning to Fail Better: Reflections on the Challenges and Risks of Community-Based Participatory Mental Health Research With Inuit Youth in Nunavut
Bibliographic record
Abstract
Community-based participatory research (CBPR) is a mine field of moral dilemmas. Even when carefully planned for and continuously critically reflected upon, conflicts are likely to occur as part of the process. This paper illustrates the lessons learned from "Building on Strengths in Naujaat", a resiliency initiative with the objective of promoting sense of belonging, collective efficacy, and well-being in Inuit youth. Naujaat community members over time established strong meaningful relationships with academic researchers. Youth took on the challenge of organizing community events, trips out on the land, and fundraisers. While their creativity and resourcefulness are at the heart of the initiative, this paper explores conflicts and pitfalls that accompanied it. Based on three themes - struggles in coming together as academic and community partners, the danger of perpetuating colonial power structures, and the challenges of navigating complex layers of relations within the community - we examine the dilemmas unearthed by these conflicts, including an exploration of how much we as CBPR researchers are at risk of reproducing colonial power structures. Acknowledging and addressing power imbalances, while striving for transparency, accountability, and trust, are compelling guiding principles needed to support Indigenous communities on the road toward health equity.
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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 | 0.081 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.064 | 0.063 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.010 | 0.017 |
| 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".