Applying Community Based Participatory Practices to Recruitment for the Asian Cohort for Alzheimer’s Disease
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) involves many risk and protective factors encompassing genetics and lived experiences. Despite disease heterogeneity, inclusion of Asian ancestry lags. In the National Alzheimer’s Coordinating Center (NACC) registry, only 2.5% of patients are of Asian ancestry. Lack of inclusion is even greater in the Alzheimer Disease Genetic Consortium, where Asian‐Americans represent only 0.7% of the population, 7‐fold less than the population (5%) of Asian‐Americans over 65. Cumulatively, disparity in research negatively impacts understanding of AD risk and protective factors and led to the formation of the Asian Cohort for Alzheimer’s Disease (ACAD) to address this lack of inclusion. Method Given the paucity of Asian American and Canadian older adults in AD research, ACAD applies a community‐based participatory research (CBPR) program to recruit participants, involving research‐community‐medicine partnerships to facilitate both better health outcomes and inclusion in research. A comprehensive program of brain health education, destigmatization, and bridging access to care are important aspects developed through ACAD Outreach and in Toronto. Extensive partnerships also help drive greater awareness about AD and related dementias to ensure a thriving dialog in the community. Result Since September 2021, the Toronto site has had interest from over 300 individuals of Chinese descent, successfully prescreened 2/3 of those and have thus far enrolled half in the study. Of completed participants, ∼50% are cognitively healthy, 20% with AD and 27% MCI, and 2/3 of participants have provided blood, despite societal reticence for blood donations. To achieve recruitment, we conducted multiple community events, including 15 virtual seminars, in addition to working with medical, long‐term care and community center partners, and using traditional print and social media. Tools to effectively community brain health education and ACAD were developed, including overcoming challenges to participation ensuring participant comfort. This has led to high referral rates (20%) indicating strong traction in the community. Conclusion ACAD has demonstrated feasibility in recruitment of voluntary participants to study the risk and protective factors of AD amongst Asian populations and will continue to recruit as well as analyze data, providing this information back to the community as part of the CBPR tenet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.203 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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; the direct Gemma label and the distilled Codex classifier 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".