Developing best practices for outreach for the Asian Cohort for Alzheimer’s Disease (ACAD) study
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a leading cause of death worldwide. Despite the global impact, clinical research in AD and, in particular, clinical trials, biomarker and genome‐wide association studies are underrepresented for racial/ethnic minorities such as Asian Americans and Asian Canadians (ASAC). The Asian Cohort for Alzheimer’s Disease (ACAD) is the first large AD cohort for ASAC to address this disparity, building on best practices in community recruitment and outreach strategies with the goal of identifying genetic and non‐genetic risk factors of AD in populations of Asian descent. ACAD will include individuals of Chinese, Korean and Vietnamese ancestry, representing the first multi‐cultural Asian cohort analysis for AD. Method Community‐Based Participatory Research (CBPR) principles are embedded through all activities as a fundamental strategy to increase research participant engagement of ASAC. CBPR engages the community in all phases of research (i.e., recruitment; resource sharing; study design; data interpretation) as a collaborative process between the community and researchers that leverages each other’s strengths/assets, and requires commitment to sustainability. ACAD’s Recruitment and Outreach Workgroup aims to: a) coordinate and monitor recruitment activities across all 8 recruiting sites in the US and Canada; b) develop and disseminate recruitment and community outreach/education material; c) work with ACAD’s community advisory board (CAB) to address community partner needs; d) work with AD and Asian community partners; and e) develop best practices for community outreach in Asian and immigrant populations. Result We have gathered local/regional/national leaders representing the three target ASAC cultural groups (Chinese, Korean, and Vietnamese) of ACAD as our CAB. Four significant activities thus far include: curation of information for public use; translation of ACAD educational/outreach materials; development and dissemination of social media information; and, identifying outreach and enrollment metrics as enduring documents for the research and community stakeholders. Conclusion The overarching goal of ACAD is to include this underserved and underrepresented group, reducing health disparities, while increasing our knowledge about the risk of AD among ASAC. Our multifaceted outreach and recruitment strategies will help to optimize ACAD’s success in recruitment and engagement with the ASAC community.
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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 | 0.279 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".