Impact of mobile research unit in SKYLINE trial to broaden trial access in traditionally underrepresented communities
Bibliographic record
Abstract
Abstract Background The SKYLINE prevention trial (NCT05256134) aimed to enroll cognitively unimpaired, elderly, amyloid‐positive adults, which is known to be challenging. Additionally, it is essential to address disparities of access to trials, particularly in underrepresented populations (URPs) where the need to travel to a research site and lack of comfort or distrust of medical providers have been identified as barriers to participation (Alzheimers Dement 2021;17:327‐406). Potential participants in URPs may have limited experience/knowledge of clinical research; therefore, additional community‐based initiatives were launched to improve awareness and education. Method A mobile research unit (THOR) was designed to visit selected communities and provide education on memory loss and initial access to screening for the SKYLINE trial. THOR was designed to optimize participant experience and study visit flow (Figure), and was strategically operationalized in Florida due to the large prevalence of older adults, high proportions of URPs, and concentration of Alzheimer’s research centers. Two multilingual personnel, representative of the targeted populations and with expertise in outreach within URPs, managed the unit. Community‐based settings were targeted, including community centers, churches, retirement communities, restaurants, and doctors’ offices. In addition to SKYLINE prescreening, THOR offered community‐based memory screenings and memory loss education to help build relationships and trust with communities. A survey was used to assess site staff perceptions of the implementation of THOR. Result Between July and November 2022, THOR was deployed at 20 community outreach events; 325 participants were prescreened for SKYLINE (Table 1). Race and ethnicity data were provided by 127 participants at 12 events from Global Alzheimer’s Platform (GAP) Foundation‐partnered sites; 64/127 (50.4%) participants were from an URP. Overall, 80% of surveyed site staff agreed/strongly agreed that THOR had a positive impact on relationship‐building and prescreening rates in URPs (Table 2). Conclusion THOR was an innovative, strategic way to increase access to SKYLINE by raising awareness of brain health and clinical research in the community. Site survey responses indicated utilization of THOR improved engagement with URPs. Implementation of a mobile research unit with capabilities to perform trial prescreening activities was an effective way to engage potential trial participants including URPs.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".