Draw-Care, a Co-Designed Multilingual Digital Intervention for Family Carers of People Living With Dementia From Ethnically Diverse Communities: User-Testing Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".