Effectiveness of Interventions for Addressing Digital Exclusion in Older Adults in the Social Care Domain: Rapid Review
Notice bibliographique
Résumé
BACKGROUND: Older adults make up the largest proportion of nonusers of the internet. With the increasing digitalization of services, it is important to identify what interventions are effective at reducing digital exclusion in older adults. OBJECTIVE: We aimed to identify what evidence exists on the effectiveness of interventions to address digital exclusion in older adults. METHODS: This rapid review assessed the effectiveness of interventions to address digital exclusion in older adults aged 60 years or older. Searches were conducted in November 2023 across a range of databases and used supplementary search methods. Searches were limited to comparative studies published from 2018 onward in English. Data were analyzed using a narrative synthesis approach. RESULTS: A total of 21 studies were included that aimed to increase a range of digital literacy skills. Sample sizes ranged from 5 to 381. Intervention approaches varied considerably and were often multicomponent and undertaken in a variety of settings. There is evidence to suggest that a range of interventions can reduce physical, personal, and perceptual barriers and improve older adults' skills, knowledge, digital literacy, and perceived self-efficacy, reduce technophobia, and increase use of technology. Importantly, findings indicated improvements among a range of subpopulations, including those living in rural areas, at risk of social isolation, who are homebound, of lower socioeconomic groups, and individuals with visual impairment. To achieve improved and sustained digital inclusion in older adults, evidence suggests it may be important to ensure structural barriers, such as access to the internet and affordability of devices, are removed. However, all studies contained methodological limitations and may not be adequately powered to determine effectiveness. CONCLUSIONS: The evidence shows the potential benefits of interventions aimed at improving a range of digital skills and increasing technology use in older adults, which could help to address digital exclusion. The findings of this rapid review can inform the development and delivery of future interventions. However, it is important to consider the context in which the included interventions were used and the lack of certainty of the findings. This review also identified a lack of high-quality evidence, as all studies identified contained methodological limitations and may not have been adequately powered to determine effectiveness. In addition, consideration should also be given to those who do not wish to engage with the online world to ensure they are not left behind.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».