Engaging Racialized Newcomers in Chronic Disease Prevention and Management: A Scoping Review of Health Promotion Interventions
Bibliographic record
Abstract
Abstract Background Health promotion programs targeting chronic disease prevention and management (CDPM) are crucial for mitigating the growing the burden of chronic disease and addressing health disparities and preventable risk factors in diverse populations. Racialized newcomers face a disproportionate risk of chronic disease and encounter barriers in accessing healthcare and engaging fully in such programs, hence further compounding health disparities. Engagement is defined as a multifaceted concept that frames involvement through the lens of recruitment, retention, adherence and full participation in an intervention. While culturally tailored programs have demonstrated promise once individuals are engaged, there is a gap in literature synthesizing engagement strategies for these populations in CDPM interventions. Objective This scoping review aims to synthesize evidence on the engagement strategies used in CDPM interventions for racialized newcomers in Western countries. Methods We followed Arksey & O’Malley’s (2005) framework along with refinements by Levac et al. (2010), screening 3,898 articles across multiple databases. Data were extracted using the Elicit AI tool along with verification of extraction from research team members. Descriptive analysis summarized findings. Results Forty-eight studies were included. Most studies focused on Hispanic/Latino and East/Southeast Asian populations in the USA. Common engagement strategies included culturally tailored content, use of community health workers (CHWs), accessible materials, financial incentives, flexibility, and co-creation with community input. Conclusion Culturally relevant and community-driven strategies, including CHWs, and creative and flexible program formats, are key to engaging racialized newcomers in health promotion programs. Addressing barriers and involving communities in program design can improve participation and outcomes in chronic disease prevention and management.
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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 | 0.023 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".