Draw-Care, a Co-Designed Multilingual Digital Intervention for Family Carers of People Living With Dementia From Ethnically Diverse Communities: User-Testing Study
Notice bibliographique
Résumé
BACKGROUND: Technology can deliver culturally and linguistically appropriate resources to support ethnically diverse family carers (hereafter referred to as carers) of people living with dementia. However, carers' involvement in research on the development and evaluation of such digital health interventions is limited. OBJECTIVE: This study aims to user-test the co-designed Draw-Care multilingual, web-based dementia resource with and for carers. METHODS: We evaluated the web-based resource through observation sessions to collect carer feedback, using a mixed methods approach. This comprised the online, validated eHealth Literacy Scale and survey questions assessing the perceived usefulness and importance of the internet for health-related decision-making and access to health resources. In addition, "think-aloud" website navigation sessions were conducted, and Hotjar analytics were used to capture participants' behavior on the website. Quantitative and analytics data were analyzed descriptively, and qualitative data were analyzed using instant data analysis, followed by thematic analysis. RESULTS: Between March and April 2023, a total of 30 carers participated in the user-testing sessions (women, n=20, 67%; mean age 61, SD 13.5 years). The mean eHealth Literacy Scale score was 30 (SD 6.1). Overall, 18 (60%) participants perceived the internet as useful, and laptops and tablets were the most commonly used devices for accessing resources, each used by 9 (30%) participants. Vietnamese (n=5, 17%), Mandarin (n=5, 17%), and English (n=5, 17%) were the top 3 languages the resource was accessed in. A total of 28 (93%) participants could navigate and log in to the website with little to no support. Qualitative results showed that overall, the Draw-Care web-based resource was acceptable, culturally responsive, engaging, and usable. However, navigation was more complicated for those using smaller screens (eg, smartphones and tablets). There were linguistic discrepancies arising from translation issues in Vietnamese and Mandarin, and 14 (46%) participants found it difficult to identify and use the chatbot (ie, virtual helper interface). Issues identified with the prototype Draw-Care website and the proposed improvements included eliminating the virtual helper, simplifying the rating scale from a 5-point smiling emoticon scale to a 3-star rating scale, improving the visibility of the feedback button, and ensuring translation accuracy. CONCLUSIONS: To our knowledge, this is the first study to evaluate a bespoke multilingual website delivering a novel, co-designed, and culturally adapted digital intervention in 10 languages for ethnically diverse family carers of people living with dementia. Findings from this user-testing study undertaken with carers uncovered usability issues requiring remediation and emphasized the importance of inclusive, accessible, culturally sensitive, engaging, and beneficial content and design. Key revisions were implemented before the randomized controlled trial commenced. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1177/20552076231205733.
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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».