Engaging Community-Dwelling Older Adults in Research: Qualitative Substudy of Factors Impacting Participation
Bibliographic record
Abstract
BACKGROUND: Innovative approaches to community-level data collection are crucial to inform policies and programs that support people in aging well within their communities. For example, community-level data can proactively identify unmet health needs, inform preventative care strategies, and ensure the equitable distribution of resources that enable older adults to age in place. OBJECTIVE: This paper presented a substudy of a larger community-based project designed to identify community-dwelling older adults' concerns about their well-being and connect them with resources to help them age well at home. The substudy aimed to identify motivations that influence older adults' engagement in research and barriers to their participation. METHODS: Data collection involved qualitative semistructured interviews with 27 older adults, with a mean age of 77 (SD 5.4), who had completed a comprehensive assessment. Purposeful sampling prioritized older adults who lived in rural areas, had more than one health condition, and represented diverse ethnicities, while attempting to reach equal numbers of participants across the participating communities. Interviews were conducted by trained research team members using an interview guide focused on reasons for research participation and perceptions of the assessment and resource action plan. Meeting minutes, gathered during 35 biweekly or monthly sessions with community coordinators, captured real-time reflections on recruitment processes, challenges, and community-specific factors influencing participation. Thematic analysis was completed using both inductive and deductive approaches. RESULTS: Older adult participants were primarily female (n=22, 82%), of European (n=19, 70%) or Acadian (n=8, 30%) descent, university educated (n=14, 52%), with one or more chronic health conditions (n=26, 96%). Older adults reported 2 main reasons for participating: planning for the future and helping their community. At the same time, barriers to participation identified included communication challenges, fear of scams, and institutional skepticism. Participants emphasized a desire for practical outcomes from the research, especially related to aging-in-place supports. Although trust in local, personal relationships facilitated participation, skepticism toward institutions and digital communication channels were barriers to participation. CONCLUSIONS: This research highlighted the need to tailor communication strategies to older adults by understanding factors influencing engagement. Addressing institutional skepticism and leveraging trusted community members are possible strategies to overcome barriers to successful engagement in community-based research. These findings advance our understanding of why older adults participate in research and suggest ways to improve recruitment strategies. Participation was motivated not only by personal benefit but also by a strong sense of civic responsibility, social connection, and a desire to contribute to future community well-being. Framing research as community-driven and future-oriented, rather than problem- or deficit-based, studies can resonate more deeply with older adults. Integrating research within existing, trusted local networks and venues helps build legitimacy and accessibility-especially in rural contexts where institutional trust may be low and digital communication less effective.
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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 | 0.035 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".