Meaningful and deep community engagement efforts for pragmatic research and beyond: engaging with an immigrant/racialised community on equitable access to care
Bibliographic record
Abstract
Primary healthcare access is one of the crucial factors that ensures the health and well-being of a population. Immigrant/racialised communities encounter a myriad of barriers to accessing primary healthcare. As global migration continues to grow, the development and practice of effective strategies for research and policy regarding primary care access are warranted. Many studies have attempted to identify the barriers to primary care access and recommend solutions. However, top-down approaches where the researchers and policy-makers 'prescribe' solutions are more common than community-engaged approaches where community members and researchers work hand-in-hand in community-engaged research to identify the problems, codevelop solutions and recommend policy changes. In this article, we reflect on a comprehensive community-engaged research approach that we undertook to identify the barriers to equitable primary care access among a South Asian (Bangladeshi) immigrant community in Canada. This article summarised the experience of our programme of research and describes our understanding of community-engaged research among an immigrant/racialised community that meaningfully interacts with the community. In employing the principles of community-based participatory research, integrated knowledge translation and human centred design, we reflect on the comprehensive community-engaged research approach we undertook. We believe that our reflections can be useful to academics while conducting community-engaged research on relevant issues across other immigrant/racialised communities.
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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 | 0.086 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.044 | 0.068 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.043 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".