Perspectives on Technology Use in the Context of Caregiving for Persons With Dementia: Qualitative Interview Study
Notice bibliographique
Résumé
BACKGROUND: Examining ways to support persons with dementia and their caregivers to help minimize the disease's impact on individuals, families, and society is critical. One emerging avenue for support is technology (eg, smartphones and smart homes). OBJECTIVE: Given the increasing presence of technology in caregiving, it is pertinent to appreciate whether and how technology can be most useful to a care partner's everyday life. This study aims to further understand care partner technology use, attitudes, and the potential role of off-the-shelf technologies (eg, smartphones and smart homes) in supporting caregiving from the perspective of care partners for persons with dementia. METHODS: We conducted a telephone cross-sectional survey using random digit dialing with 67 self-identified care partners of persons with dementia across one Canadian province. Participants were asked about attitudes toward technology, barriers to and facilitators for technology use, technology use with caregiving, and demographic information. Eight open-ended questions were analyzed using content analysis; 2 closed-ended questions about comfort with and helpfulness of technology (rated on a scale of 1 to 10) were analyzed with frequencies. From these data, an in-depth semistructured interview was created, and 10 (15%) randomly sampled care partners from the initial collection of 67 care partners were interviewed approximately 1 year later, with responses analyzed using content analysis. RESULTS: Frequency analysis rated on a scale of 1 to 10 suggested that care partners were comfortable with technology (wearable technology mean 7.94, SD 2.02; smart home technology mean 6.94, SD 2.09), although they rated the helpfulness of technology less strongly (mean 5.02, SD 2.85). Qualitatively, care partners described using technology for functional tasks and some caregiving. Barriers to technology use included cost, lack of knowledge, security or privacy concerns, and undesirable features of technology. Facilitators included access to support and the presence of desirable features. Some care partners described merging technology with caregiving and reported subsequent benefits. Others stated that technology could not be adopted for caregiving due to the degree of impairment, fear of negative consequences for the person living with dementia, or due to incongruity with the caregiving philosophy. Furthermore, care partners noted that their technology use either increased or was unchanged as they moved through the COVID-19 pandemic. CONCLUSIONS: The 2 analyses were conducted separately, but there was notable overlap in the data, suggesting temporal stability of identified content. Both analyses suggested care partners' relative comfort with technology and its use, but other care partners noted concerns about integrating technology and caregiving. Care partners' reports of increased technology use throughout the COVID-19 pandemic may also suggest that the pandemic impacted their perceptions of the usefulness of technology, being influenced by the requirements of their reality. Future investigations should examine how to support care partners in adopting relevant technology.
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 | 0,014 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,006 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 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 ».