Refining a Digital Therapeutic Platform for Home Care Agencies in Dementia Care to Elicit Stakeholder Feedback: Focus Group Study With Stakeholders
Notice bibliographique
Résumé
BACKGROUND: Persons living with dementia require increasing levels of care, and the care model has evolved. The Centers for Medicare and Medicaid Services is transitioning long-term care services from institutional care to home- or community-based services, including reimbursement for nonclinical services. Although home care companies are positioned to handle this transition, they need innovative solutions to address the special challenges posed by caring for persons living with dementia. To live at home longer, these persons require support from formal caregivers (FCGs; ie, paid professionals), who often lack knowledge of their personal histories and have high turnover, or informal caregivers (eg, family or friends), who may have difficulty coping with behavioral and psychological symptoms of dementia. The Generation Connect platform was developed to support these individuals and their formal and informal caregivers. In preliminary studies, the platform improved mood and influenced caregiver satisfaction. To enhance platform effectiveness, Generation Connect received a grant from the National Institutes of Health Small Business Innovation Research to improve clinical outcomes, reduce health care costs, and lower out-of-pocket costs for persons living with dementia who receive care through home care agencies. OBJECTIVE: This study aims to evaluate information elicited from a series of stakeholder focus groups to understand existing processes, needs, barriers, and goals for the use of the Generation Connect platform by home care agencies and formal and informal caregivers. METHODS: A series of focus groups were conducted with home care agency corporate leadership, home care agency franchise owners, home care agency FCGs, and informal caregivers of persons living with dementia. The qualitative approach allowed for unrestricted idea generation that best informed the platform development to enable home care providers to differentiate their dementia care services, involve informal caregivers, improve FCG well-being, and extend the ability of persons living with dementia to age in place. Using the Technology-Enabled Caregiving in the Home framework, an inductive and iterative content analysis was conducted to identify thematic categories from the transcripts. RESULTS: Overall, 39 participants participated across the 6 stakeholder focus groups. The following five overarching themes were identified: technology related; care services; data, documentation, and outcomes; cost, finance, and resources; and resources for caregivers. Within each theme, the most frequent subthemes were identified. Exemplar stakeholder group statements provided support for each of the identified themes. CONCLUSIONS: The focus group results will inform the further development of the Generation Connect platform to reduce the burden of caregiving for persons living with dementia, evaluate changes in cognition, preserve functional independence, and promote caregiver engagement between these individuals. The next step is to evaluate the effectiveness of the revised platform in the National Institutes of Health Small Business Innovation Research phase 2 clinical trial to assess the efficacy of its evidence-based interventions and market viability.
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 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 ».