Social Prescription Interventions Addressing Social Isolation and Loneliness in Older Adults: Meta-Review Integrating On-the-Ground Resources
Notice bibliographique
Résumé
BACKGROUND: Social prescription programs represent a viable solution to linking primary care patients to nonmedical community resources for improving patient well-being. However, their success depends on the integration of patient needs with local resources. This integration could be accelerated by digital tools that use expressive ontology to organize knowledge resources, thus enabling the seamless navigation of diverse community interventions and services tailored to the needs of individual users. This infrastructure bears particular relevance for older adults, who experience a range of social needs that impact their health, including social isolation and loneliness. An essential first step in enabling knowledge mobilization and the successful implementation of social prescription initiatives to meet the social needs of older adults is to incorporate the evidence-based academic literature on what works, with on-the-ground solutions in the community. OBJECTIVE: This study aims to integrate scientific evidence with on-the-ground knowledge to build a comprehensive list of intervention terms and keywords related to reducing social isolation and loneliness in older adults. METHODS: A meta-review was conducted using a search strategy combining terms related to older adult population, social isolation and loneliness, and study types relevant to reviews using 5 databases. Review extraction included intervention characteristics, outcomes (social [eg, loneliness, social isolation, and social support] or mental health [eg, psychological well-being, depression, and anxiety]), and effectiveness (reported as consistent, mixed, or not supported). Terms related to identified intervention types were extracted from the reviewed literature as well as descriptions of corresponding community services in Montréal, Canada, available from web-based regional, municipal, and community data sources. RESULTS: The meta-review identified 11 intervention types addressing social isolation and loneliness in older adults by either increasing social interactions, providing instrumental support, promoting mental and physical well-being, or providing home and community care. Group-based social activities, support groups with educational elements, recreational activities, and training or use of information and communication technologies were the most effective in improving outcomes. Examples of most intervention types were found in community data sources. Terms derived from the literature that were the most commonly congruent with those describing existing community services were related to telehealth, recreational activities, and psychological therapy. However, several discrepancies were observed between review-based terms and those addressing the available services. CONCLUSIONS: A range of interventions found to be effective at addressing social isolation and loneliness or their impact on mental health were identified from the literature, and many of these interventions were represented in services available to older residents in Montréal, Canada. However, different terms were occasionally used to describe or categorize similar services across data sources. Establishing an efficient means of identifying and structuring such sources is important to facilitate referrals and help-seeking behaviors of older adults and for strategic planning of resources.
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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 | 0,007 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,012 | 0,016 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».