Assistive technologies that support social interaction in long-term care homes: a scoping review
Notice bibliographique
Résumé
ABSTRACT Objective: The objective of this review was to chart the literature on assistive technologies (excluding robots) that support social interaction of older adults in long-term care homes, and to advance a definition of socially assistive technologies. Introduction: Loneliness and social isolation have adverse effects on the health and well-being of older adults. Many long-term care homes provide recreational programming intended to entertain or distract residents, yet the evidence of their effectiveness is limited. Absent from the literature are comprehensive reviews of assistive technologies (other than robots) that are used to support social interaction in long-term care homes. Inclusion criteria: The review considered research studies as well as gray literature that included older adults (≥65 years) living in long-term care homes. The concept of interest was the use of assistive technologies (excluding robots) that support social interaction in long-term care homes. Methods: The databases were searched on June 26, 2019, and included CINAHL Full Text (EBSCO), MEDLINE (Ovid), PsycINFO (EBSCO), Sociological Abstracts (ProQuest), Embase (Elsevier), and Web of Science (Clarivate). The search for gray literature was conducted in ProQuest Dissertations and Theses Databases and across 11 websites during September and October 2019. The recommended JBI approach to study selection, data extraction, and data synthesis was used. Results: Twenty-five articles were included in this review, with comparable numbers of quantitative (n = 6), qualitative (n = 9), and mixed methods (n = 7) studies, with the remaining articles employing non-empirical designs (n = 3). Technologies were categorized as low (easily recognizable to everyone), medium (more electronics), or high (involves internet). Two studies reported on low-assistive technologies, including videotapes and the telephone. Medium-assistive technologies were identified in nine studies and included videophones; Nintendo Wii; tablet-based games; picture- and video-viewing tools; and CRDL (pronounced “cradle”), a special instrument that translates touch into sound. More than half (n = 14) of the included articles utilized high-assistive technologies, such as computer labs/kiosks, tablet-based applications, social media (eg, Facebook), videoconferencing, and multi-functional systems. Five studies measured whether assistive technologies had an impact on the quantity of long-term care residents’ social interaction levels. Qualitative themes were related to residents’ social connections and experiences after using various technologies. Four studies systematically incorporated a framework/model, and Social Structuration Theory was considered the most comprehensive. In the absence of a definition of socially assistive technologies, the definition advanced from this review is as follows: Socially assistive technologies are user-appropriate devices and tools that enable real-time connectivity to enhance social interaction. Conclusions: Included literature reported the benefits of technology use, with considerable variability in engagement and no cost estimates. We recommend that future research continue to advance our definition of socially assistive technologies, make promising assistive technologies available in long-term care homes after studies are completed, report the costs of assistive technologies, and include participants with dementia and culturally and linguistically diverse backgrounds.
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 | 0,008 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,019 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».