Culturally Adapting the World Health Organization Digital Intervention for Family Caregivers of People With Dementia (iSupport): Community-Based Participatory Approach
Bibliographic record
Abstract
BACKGROUND: Informal caregivers of people with dementia are at high risk of developing mental and physical distress because of the intensity of the care provided. iSupport is an evidence-based digital program developed by the World Health Organization to provide education and support for the informal everyday care of people living with dementia. OBJECTIVE: Our study aims to describe in detail the cultural adaptation process of iSupport in Switzerland. We specifically focused on the participatory strategies we used to design a culturally adapted, Swiss version of iSupport that informed the development of the desktop version, mobile app, and printed manual. METHODS: We used a mixed methods design, with a community-based participatory approach. The adaptation of iSupport followed the World Health Organization adaptation guidelines and was developed in 4 phases: content translation, linguistic and cultural revision by the members of the community advisory board, validation with formal and informal caregivers, and refinement and final adaptation. RESULTS: The findings from each phase showed and consolidated the adjustments needed for a culturally adapted, Swiss version of iSupport. We collected feedback and implemented changes related to the following areas: language register and expressions (eg, from "lesson" to "chapter" and from "suffering from" dementia to "affected by" dementia), resources (hyperlinks to local resources for dementia), contents (eg, from general nonfamiliar scenarios to local and verisimilar examples), graphics (eg, from generalized illustrations of objects to human illustrations), and extra features (eg, a glossary, a forum session, and a read-aloud option, as well as a navigation survey). CONCLUSIONS: Our study provides evidence on how to culturally adapt a digital program for informal caregivers of people living with dementia. Our results suggest that adopting a community-based participatory approach and collecting lived experiences from the final users and stakeholders is crucial to meet local needs and to inform the further development, testing, and implementation of digital interventions in a specific cultural context.
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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 | 0.039 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".