The challenges of aging in place for older adults at risk of self‐neglect and expectations regarding smart environments: Perspectives of older adults and their caregivers
Notice bibliographique
Résumé
Abstract Background Self‐neglect is a behavior found in older adults with cognitive deficits. It manifests through the refusal, or inability, to care for themselves and puts them at higher risk for institutionalization. In Quebec, administrators in home care services are increasingly open to offering smart environments as part of their services to this clientele as it can help identify unfulfilled needs. However, little information is available about the implementation of technology in home care services, including the perspective of this clientele and their family. An embedded case‐study was conducted to provide insight on their perception of obstacles for home care and their expectations of technological solutions. Method Through individual interviews, qualitative data regarding the perspectives of older adults at risk of self‐neglect (n = 5) and their caregivers (n = 3) was collected in regard to: 1) current obstacles at home; and 2) expectations toward technology to optimize their functional autonomy. Data was analyzed using the approach of Miles, Huberman and Saldana. Result Despite the fact that some older adults (n = 3) presenting self‐neglect perceive themselves as functioning well at home and not needing more help than they already receive from home care service, all older adults (n = 5) perceived risks in their home environment and described difficulties with their memory (e.g. forgetting appointments) and mobility (e.g. falls). Similar difficulties were described by their caregivers, who also perceived some risks with remaining at home due to a deteriorating functional capacity. With respect to technology, it was identified that it could support the autonomy of the older adults in some aspects, but not replace current home services available. Technology also had to be easy to use and learn. Conclusion This study suggests that older adults at risk of self‐neglect and their caregivers believe that technology can support aging in place but in combination with other solutions. When implemented, technology should be adapted to the needs, values, and preference of clients and their family, to address as many of the individual’s perceived risks as possible.
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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».