Subject Matter Expert Insights for Engaging Underrepresented Older Adults with Disabilities in Research
Bibliographic record
Abstract
Abstract The population of older Americans is increasingly diverse. To keep pace with this demographic shift and advance gerontological research, it is critical that research samples reflect this diversity, so findings are generalizable and representative. However, there are barriers to engaging individuals from underrepresented racial/ethnic groups, such as a mistrust of government institutions, historical precedence of unethical research, language barriers, and logistical challenges that often impede participation. There are some general recommendations for expanding diversity of research samples and overcoming these barriers, but there is a need to understand more about best practices for older adults with disabilities, which is a population that can be especially challenging to reach. We interviewed Subject Matter Experts (SMEs) who had direct experience (professional or personal) engaging older adults and/or people with disabilities from underrepresented racial/ethnic groups (e.g., Black/African American, Asian American, Hispanic/Latino American, and Native American). Participants (N = 13) included researchers, clinicians, and advocacy group leaders. The SMEs shared insights on effective strategies for recruitment, outreach, study design, and knowledge dissemination. Their recommendations had key themes including establishing reciprocal community partnerships, involving community members on the research team, offering flexible ways to participate, and carefully considering terminology when referring to disability, race, and ethnicity. These interviews with SMEs provided important insights and actionable solutions to improve the diversity and inclusivity of research samples. Ensuring that participant samples reflect the range of persons who will use a product, technology, intervention, or community program will contribute to successful implementation, ultimately improving quality of life for older adults.
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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 | 0.083 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".