Navigating Between Rigour and Community-Based Research Partnerships: Building the Evaluation of the Uniting Our Nations Health Promotion Program for FNMI Youth
Bibliographic record
Abstract
Our team has worked closely with First Nations, Métis, and Inuit (FNMI) community partners and our local school board over the past 9 years to develop a range of strengths-based health promotion programs for FNMI youth. This article begins with a brief description of our school-based programming to provide context. Next, we identify challenges in conducting rigorous program evaluation and highlight the requirements of community-based research partnerships. Finally, we identify a number of factors that have helped us achieve a balance between the frameworks of rigour and community-based research partnerships. Throughout the paper we use examples from our projects to illustrate issues.
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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 | 0.451 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.005 | 0.008 |
| 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".