Understanding Current Needs and Future Expectations of Informal Caregivers for Technology to Support Health and Well-being: National Survey Study
Notice bibliographique
Résumé
BACKGROUND: There are approximately 6.5 million informal (unpaid) caregivers in the United Kingdom. Each caregiver plays a critical role in the society, supporting the health and well-being of those who are ill, disabled, or older and who need frequent support. Digital technologies are becoming a ubiquitous part of everyday life for many, but little is known about the real-world impact of technology for those in a caring role, including the abilities of technologies to address the mental and physical impacts of caregiving. OBJECTIVE: This study aims to understand the current and future technology use of caregivers, including digital technologies used to care for themselves and the person they look after. METHODS: We codeveloped a wide range of questions with caregivers and care professionals and delivered this survey both on the web and in paper format (eg, using social networks such as Twitter alongside in-person events). Questions were focused on providing care and looking after caregiver health and well-being. Analyses focused on both quantitative outcomes (frequency counts and Likert questions) and explored free text entries (thematic analysis). RESULTS: From 356 respondents, we identified that caregivers were receptive to, and largely positive about current and future use of technology both for their own care and their caring role (eg, checking in from distance). There were notable concerns, including the risk that technology could replace human contact. We identified several key areas for future work, including communication with health and social care professionals, and the potential for technology to help caregivers with their own health. We also identified several stakeholders (eg, care workers, pharmacy staff, and general practitioners) who could act as suitable points for technology signposting and support. CONCLUSIONS: Caregivers are a transient, often difficult to reach population, and this work has collated a large body of knowledge across a diverse group of individuals. Many caregivers, like the rest of society, are realizing the benefits of using everyday technology to help deliver care. It is clear that there is already a high level of dependency on technologies, where future expectations will grow. However, many barriers to digital technology use remain, including a lack of ongoing technology support. Preventive measures linked to technology that can help look after a caregiver's own health appear acceptable, particularly for communicative tools. This collated caregiver knowledge is a call for all stakeholders-academics, policy makers, and practitioners-to take note of these specific challenges, and to ensure that caregiver voices are both heard and fully integrated within the emerging digital health agenda.
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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 | 0,000 | 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 ».