Engaging Community-Dwelling Older Adults in Research: Qualitative Substudy of Factors Impacting Participation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,035 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,010 | 0,008 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».