Adapting a Dutch Web-Based Intervention to Support Family Caregivers of People With Dementia in the UK Context: Accelerated Experience-Based Co-Design
Bibliographic record
Abstract
BACKGROUND: Around 700,000 family caregivers provide unpaid care for 900,000 people living with dementia in the United Kingdom. Few family caregivers receive support for their own psychological needs and funding for community respite services has declined. These trends are seen across Europe as demographic and budgetary pressures have intensified due to public spending cuts arising from the 2008 financial crisis and the COVID-19 pandemic. The World Health Organization has prioritized the need to expand the provision of support for caregivers and families of people with dementia by 2025. Web-based interventions have the potential for development as they require modest investment and can be accessed by family caregivers at home. Further cost benefits can be realized by adapting existing interventions with demonstrated effectiveness for new contexts. This paper reports initial findings from the CareCoach study, which is adapting Partner in Balance (PiB), a web-based coaching intervention developed in the Netherlands, for family caregivers in the United Kingdom. OBJECTIVE: This study aims to work with unpaid family caregivers and staff in adapting the Dutch web-based support tool PiB to improve its acceptability and usability for use in the United Kingdom. METHODS: Accelerated Experience-Based Co-Design (AEBCD) was used with caregivers, staff, and core stakeholders. Interviews, workshops, and stakeholder consultations were conducted. Data were analyzed iteratively. Recommendations for the redesign of PiB for use across the United Kingdom were adjudicated by the study Adaptation Working Party. RESULTS: Sixteen caregivers and 17 staff took part in interviews. Thirteen caregivers and 17 staff took part in workshops. Most (n=26) participants were White, female, and retired. All except 4 caregivers (2 male and 2 female) found the PiB's offer of web-based self-help learning acceptable. Caregivers identified complexity and lack of inclusivity in some wording and video resources as problematic. The staff took a stronger perspective on the lack of inclusivity in PiB video resources. Staff and caregivers coproduced new inclusive wording and recommended creating new videos to adapt PiB for the UK context. CONCLUSIONS: AEBCD methods facilitated the engagement of caregivers and staff and advanced the adaptation of the PiB complex intervention. An important addition to the AEBCD method in this process was the work of an Adaptation Working Party, which adjudicated and agreed to new wording where this could not be established in consultation with caregivers and staff. TRIAL REGISTRATION: ISRCTN Registry ISRCTN12540555; https://doi.org/10.1186/ISRCTN12540555.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".