Perceived efficacy, feasibility, and implementation of community engaged research practices in the multi‐site SuperAging Research Initiative
Bibliographic record
Abstract
Abstract Background Studies demonstrate that community engaged research (CER) supports minority recruitment into Alzheimer’s disease studies. In contrast, few studies have explored whether CER can engage diverse ‘SuperAgers,’ adults age 80+ with superior episodic memory. Within the multi‐site SuperAging Research Initiative, which seeks to recruit 40% Black/African American adults, we assessed current CER strategies, study team perceptions of CER feasibility and effectiveness, and barriers to implementing or sustaining CER. Method The five SuperAging sites completed a literature‐informed survey on current CER practices and the perceived feasibility and effectiveness of ten potential strategies. Results All sites (100%) have a dedicated CER study team/member, while most (80%) have an ethnoracially representative study team. Responses indicated strong partnerships with community advisors (80%) and organizations (100%), with these techniques rated as highly feasible and effective. Eighty percent of sites provide community education events and find this strategy feasible and effective. In contrast, few sites (40%) publish a newsletter, with variable impressions of its effectiveness and feasibility and lack of expertise creating an implementation barrier. Despite strong perceptions of feasibility and effectiveness, only one site tailors recruitment materials for specific communities due to concerns about expertise and cost. Social media is rarely used as a source of advertising (40%); several sites questioned the feasibility and effectiveness of this technique for SuperAgers, and cited expertise and time as a barrier. Finally, though several sites (60%) see participants during evening hours, few provide early morning (40%) or weekend (20%) appointments, and only one (20%) completes study activities off‐site. Perceived feasibility of these methods was hampered by personnel and time. Conclusion SuperAging sites implement a broad range of CER techniques to increase Black/African American representation in the sample. Responses highlighted the perceived importance of diverse study teams with dedicated time for CER and bidirectional communication with the community. Respondents saw flexible study sites/schedules and tailored recruitment materials as highly effective but difficult to accomplish, making them possible targets for future study planning. While personnel, expertise, and finances represent consistent challenges, sites are universally invested in CER. These data will inform future implementation and evaluation of recruitment science of a diverse SuperAging population.
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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 | 0.093 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".