Co-Designing a Digital Platform to Support a Culturally Adapted Family Intervention (CaFI:Digital) for Psychosis Among People of Sub-Saharan African and Caribbean Descent: Agile Co-Design Approach
Bibliographic record
Abstract
Background: People of sub-Saharan African and Caribbean descent are significantly more likely to be diagnosed with psychotic disorders than other ethnic groups in the United Kingdom. The National Institute for Health and Care Excellence in the United Kingdom recommends family therapy as a clinically effective treatment for the management of psychosis. The National Institute for Health and Care Excellence also recommends that family interventions should be culturally informed to meet the needs of an increasingly ethnically diverse population. People from minoritized backgrounds are rarely offered family therapy; however, the rise in digital mental health worldwide offers unique opportunities to support culturally informed approaches at scale and at a low cost. Objective: The overarching aim of culturally adapted family intervention (CaFI):Digital was to help address inequalities in the provision of mental health care for people of sub-Saharan African and Caribbean descent, including those of Mixed heritage. A digital platform, CaFI:Digital, was built to support delivery of a CaFI. The purpose of developing CaFI:Digital was to provide an accessible, user-friendly, and engaging website for service users, their families, and therapists as an alternative or adjunct to in-person therapy. Methods: We used an iterative Agile co-design approach to develop a user-friendly and inclusive website. Co-design workshops (n=2), semistructured interviews (n=2), and collaborative research team meetings (n=3) were used to capture and prioritize end-user feedback on the clinician- and service-user-facing components of the platform. The software was developed using Agile sprints, with each sprint lasting 3 weeks, allowing feedback to be integrated rapidly and revised software prototypes to be shared with end users for review, revision, and approval. Results: Key software requirements, such as accessibility and diverse content, were identified in the co-design activities and were implemented to maximize accessibility and usability of the website. Following software development, we successfully beta-tested the software with our target end user population of service users and therapists to ensure it was defect-free and ready for use. Conclusions: A digital platform to support delivery of CaFI for psychosis was rapidly developed through a series of co-design activities. To our knowledge, this is the first bespoke digital therapy platform that has been co-designed with and for people of sub-Saharan African and Caribbean descent who experience psychosis. This is important given the disproportionate rates of diagnosis and lack of access to psychological therapies experienced by this population.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".