Levels of Engagement of South Asian Participants in Health Research: Effectiveness of Community-Based Research (CBR) Framework
Bibliographic record
Abstract
Objectives: This study aims to explore the levels of engagement of South Asian participants in health research and to assess the effectiveness of the Community-Based Research (CBR) framework in promoting health equity among this diaspora in Canada. This review also seeks to identify the effectiveness of the democratic and inclusive model of CBR in integrating marginalized communities into health research. Methods: This review was conducted following Alamgir, et al.’s supplementation of Arksey and O’Malley’s scoping review methodological framework. EBSCO CINAHL, Ovid MEDLINE, and Ovid APA PsycINFO databases were searched with assistance from a University of Toronto librarian. Open-access papers, grey literature, and articles were searched through a snowball approach following the bibliographies of collected articles. The included articles fulfilled strict inclusion criteria and were published in English between January 2004 and July 2024. The data was screened, charted, and assessed for quality, rigour, and level of community engagement. The PI supervised, reviewed, supported, and edited each step in an insider-outsider role. Results: Out of 63 articles, 20 were selected, revealing unique methodologies and interaction models to engage with South Asian communities in research. Key findings indicate that the main challenges faced in implementing CBR in the South Asian Community are trust, transparency, and perceived benefits of the end-users in the community after the research is done. The democratic and inclusive model of CBR utilizing the Asset Based Community Development Framework engages members of the community as peer researchers throughout the research process and reports back to the community to overcome these challenges. The study also identified that the most successful methods to overcome are to adhere to culturally pertinent practices and apply a collaborative, inclusive and meaningful method of community engagement. Identified weaknesses of the research include participants not being involved throughout the entirety of the project, not many studies incorporating CBR; and a smaller number of insightful studies minimizing diversity and ensuring equity in this area. Conclusion: This study identifies key challenges of research on South Asians’ health and underscores the importance of adopting the Community-Based Research framework for conducting equity-informed studies with South Asians in which community members participate as research subjects and co-researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.359 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".