Early CER Strategies to Recruit Black 80+ year olds in the SuperAging Research Initiative
Bibliographic record
Abstract
Abstract Background Community engaged research (CER) has been critical for increasing diversity in Alzheimer’s studies. However, the effectiveness of CER for engaging a diverse population of ‘SuperAgers,’ has been largely unstudied. SuperAgers are individuals age 80+ with superior episodic memory. The multi‐site SuperAging Research Initiative (SRI) seeks to recruit at least 500 SuperAgers and similarly aged controls, including 40% who identify as Black. This report summarizes initial CER strategies, implementation of new strategies, and enrollment success across the first ∼18 months. Method Semi‐structured interviews were conducted at each site to assess current CER strategies and perceptions of facilitators/barriers to CER approaches. Interviews were transcribed and concept mapping was used to identify key themes across sites. Key themes are reported here along with recruitment outcomes across the sites over the first ∼18 months. Initial responsive actions are described. Result SRI sites were selected based on their historical success engaging and recruiting Black older adults in cognitive aging research; however, identification and recruitment of SuperAgers was new for most sites. Results revealed strong current community engagement practices across sites. Emergent themes included prioritizing diverse staff, barriers to CER, strengths of the multi‐site design, the value of research to participants. Sites also implemented several CER strategies including building/enhancing community partnerships, providing community presentations, and engaging community members and enrolled participants in feedback sessions related to recruitment methods. Responsive actions of the SRI included development of participant informed culturally tailored recruitment flyers, videos, and photos, which incorporated site‐level feedback. Holiday‐themed appreciation gifts were provided to enrolled participants. Initial enrollment included 173 participants (mean age: 86.6 years). Diverse recruitment across sites ranged from 0‐27%. Diverse recruitment increased over the second half of the year, potentially suggesting early success with CER strategies. Conclusion Initial recruitment reflects a starting point for the enrollment of Black SuperAgers and Controls. Future plans involve implementing additional study‐wide CER recruitment methods, utilizing online registries as a recruitment source, and establishing ambassador boards to boost SRI enrollment of older Black adults. Strategy success will be carefully quantified, and outcomes will be shared for future research use.
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 | 0.038 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".