Developing best practices for outreach and recruitment: Updates from the Asian Cohort for Alzheimer’s disease (ACAD) study
Bibliographic record
Abstract
Abstract Background Despite investments in AD research to combat this public health crisis, racial/ethnic minorities such as Asian Americans and Asian Canadians (ASACs) remain underrepresented in AD research including clinical trials, biomarker, and genome‐wide association studies. The Asian Cohort for Alzheimer’s Disease (ACAD) is the first large AD cohort for ASACs with a focus on recruiting Chinese, Korean and Vietnamese. We present the outreach and recruitment strategies employed by the ACAD Recruitment and Outreach Workgroup (Workgroup) during the ACAD’s pilot phase. Method Community‐Based Participatory Research (CBPR) principles are embedded through all activities as a fundamental strategy to increase research participant engagement of ASACs. CBPR engages the community in all phases of research (i.e., recruitment; resource sharing; study design; data interpretation). The Workgroup was tasked with a) coordinating and monitoring recruitment activities across all 8 recruiting sites in the US and Canada; b) developing and disseminating recruitment and community outreach/education materials; c) convening the community advisory board (CAB) to elicit feedback on participant referrals, study processes, outreach/recruitment materials and to address community partner needs; d) collaborating with academic and community partners on outreach; and e) developing best practices for community outreach. Result The Workgroup developed multilingual and culturally adapted ACAD‐wide outreach, recruitment and retention materials including study flyers, presentation template materials, animated videos, e‐cards, social media accounts and ads. ACAD CAB provided valuable input regarding study procedures and materials as well as promoting ACAD to their respective communities and networks. During the ACAD pilot phase, 26 outreach activities were conducted across sites. Given that ACAD was launched during the COVID‐19 pandemic, many (n = 16) activities were conducted online and some at community centers, clinics, senior apartments, and outdoor events, with an estimated reach of 3,500+ community members. Many (62.5%) events were conducted in English (n = 17), and others in Asian languages such as Cantonese (n = 5), Korean (n = 2), Mandarin (n = 9) and Vietnamese (n = 2). Conclusion ACAD’s multifaceted approach to outreach and recruitment aims to optimize community engagement and a step towards representation of ASACs in AD studies. The Workgroup will continue to monitor outreach/recruitment metrics and provide support for overall and site‐specific community engagement needs.
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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 | 0.408 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.010 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".