CHALLENGES AND OPPORTUNITIES IN RECRUITING DIVERSE OLDER ADULTS WHO ARE FRAIL FROM THE MAPS-B STUDY
Bibliographic record
Abstract
Abstract Including diverse individuals at the research and participant levels are essential to improve the effectiveness of real-world interventions; however, there are challenges when including such individuals. Our study purpose was to report the challenges of recruiting diverse older adults for the Mapping Sedentary Behaviour study. Our methods were guided by Step 1 (“Establish Partnerships”) in the Knowledge-to-Action-Ethics Framework. We assembled a diverse team of eleven researchers, clinicians, and patient partners. To recruit a broad group of participants, we partnered with City Housing Hamilton, which provides subsidized housing for older adults. We met with the organization’s partnership development advisor who organized two recruitment orientations; 80 potential participants and returning attendees were present for both sessions. The organization provided coffee and donuts. Most attendees were from visible minorities and had visible disabilities (i.e., used a walker or cane). To build rapport, we met with attendees in groups of 5 to 6 to introduce the research team and explain the study. We recruited 13 participants (seven female, one transgender man; Morley FRAIL score≥3). Before their scheduled study visit, twelve participants dropped out citing medical mistrust (i.e., fearing unintentional medical tracking). The last participant dropped out after the initial study visit due to their family’s skepticism in research. Additionally, some individuals may have enrolled for financial incentives as they were interested in receiving immediate monetary compensation. We faced challenges when recruiting frail older adults from diverse backgrounds. Future studies should focus on developing methods to target medical mistrust with older adults and their families.
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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 | 0.132 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".