Continued Implementation and Use of a Digital Informal Care Support Platform Before and After COVID-19: Multimethod Study
Notice bibliographique
Résumé
Background: With the growing need of support for informal caregivers (ICs) and care recipients (CRs) during COVID-19, the uptake of digital care collaboration platforms such as Caren increased. Caren is a platform designed to (1) improve communication and coordination between ICs and health care professionals, (2) provide a better overview of the care process, and (3) enhance safe information sharing within the care network. Insights on the impact of COVID-19 on the implementation and use of informal care platforms such as Caren are still lacking. Objective: This study aimed to (1) identify technology developers' lessons learned from the continued implementation of Caren during COVID-19 and (2) examine pre-post COVID-19 changes in usage behavior and support functionality use of Caren. Methods: A focus group with developers of the Caren platform (N=3) was conducted to extract implementation lessons learned. Focus group data were first analyzed deductively, using the Consolidated Framework for Implementation Research domains (ie, individual characteristics, intervention characteristics, inner setting, and outer setting). Later, inductive analysis of overarching themes was performed. Furthermore, survey data were collected in 2019 (N=11,635) and 2022 (N=5573) among Caren platform users for comparing usage behavior and support functionality use. Data were analyzed using descriptive and inferential statistics. Results: Several lessons from the continued implementation of Caren during COVID-19 were identified. Those included, for example, alternative ways to engage with end users, incorporating automated user support and large-scale communication features, considering the fluctuation of user groups, and addressing data transparency concerns in health care. Quantitative results showed that the number of ICs and CRs who used Caren several times per day increased significantly (P<.001 for ICs and CRs) between 2019 (ICs: 23.8%; CRs: 23.2%) and 2022 (ICs: 35.2%; CRs: 37%), as well as the use of certain support functionalities such as a digital agenda to make and view appointments, a messaging function to receive updates and communicate with formal and informal caregivers, and digital notes to store important information. Conclusions: Our study offers insights into the influence of the COVID-19 pandemic on the usage and implementation of the digital informal care support platform Caren. The study shows how platform developers maintained the implementation during COVID-19 and which support functionalities gained relevance among ICs and CRs throughout the pandemic. The findings can be used to improve the design and implementation of current and future digital platforms to support informal care toward the "new digital normal."
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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 | 0,016 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».