Enhancing Participation of People Aged 75+ in Clinical and Applied Health Research Through Inclusive, Technology-Enabled Methodologies
Bibliographic record
Abstract
Older adults, aging 75 years and older are barely represented in clinical and applied health research because of health-related limitations, barriers to digital access, and methodological approaches. This study sought to co-design, trial and evaluate inclusive, digital ways to support the participation of older adults in health research. A mixed methods design was employed, comprising a quantitative phase (N = 50) followed by qualitative interviews (n = 12). The quantitative part highlighted that 66% of participants had access to a digital device whereas 48% of participants reported low confidence in their ability to use devices. Age sub-group analysis indicated that participants aged 80+ years, indicated significantly lower confidence and willingness to participate compared to those aged 75-79 years. The qualitative part revealed four themes: barriers vs facilitators to digital participation, importance of human support, trust and motivation, and preference for hybrid approaches. These findings demonstrate the need for practical, feasible and scalable strategies including caregiver supported digital participation, an emphasis on simplified technologies and flexible hybrid recruitment strategies. This study contributes to an emerging body of literature on inclusive methods and practical recommendations for enhancing the relevance and accessibility of health research for and with 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 | 0.065 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".