Community engagement in Canadian health and social science research: Field reports on four studies
Bibliographic record
Abstract
Community engagement is a hallmark of Canadian health and social science research, yet we lack detailed descriptions of pragmatic peer engagement possibilities. People personally affected by a study’s topic can actively contribute to design, data collection, intervention delivery, analysis, and dissemination yet the nature and scope of involvement can vary based on context. The shift from academic to community-based research teams, where peers who share participant identities assume a leadership role, may be attributed to the HIV/AIDS response where community co-production of knowledge has been a fundamental component since the epidemic’s onset. This article discusses four health and social science studies from a community-based participatory research (CBPR) framework and synthesizes the strengths and limitations of community engagement across these endeavours to offer lessons learned that may inform the design of future CBPR projects.
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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 | 0.123 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.041 | 0.018 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.004 |
| 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".