Developing a Program of Research at the Grassroots Community Level through Meaningful Community Engagement
Bibliographic record
Abstract
Background: Studies have attempted to identify the barriers to equitable health and social care access and recommend solutions, however, a community participatory approach where the cocreation of the knowledge on the issues and the solutions happens through meaningful community engagement appears to be scant. Top-down approaches where the researchers and policymakers 'prescribe' solutions are rather common than a more effective and community-centred approach where the community and researchers work hand-in-hand to identify the problems and co-develop the solutions and recommend policy changes. Methods: In this presentation, we reflect on a comprehensive community-engaged research approach that we undertook to identify the barriers to primary care access among a immigrant community in Canada. Expected results: This article informs the experience of our program of research that entails our understanding of how to engage community-based research among immigrant communities that meaningfully interacts with the community and the findings and the outcomes of the research are driven by the community. We strived towards continuous engagement, mass member outreach, community capacity building, and active knowledge dissemination. Conclusion: Employing the principles of Community Based Participatory Research (CBPR), Human Centered Design (HCD), and Integrated Knowledge Translation (IKT) we have established this pragmatic research program approach where meaningful community engagement is at the core.
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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 | 0.088 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".