Empowering Dementia Carers With an iSupport Virtual Assistant (e-DiVA) in Asia-Pacific Regional Countries: Protocol for a Pilot Multisite Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Dementia is a global public health priority with an estimated prevalence of 150 million by 2050, nearly two-thirds of whom will live in the Asia-Pacific region. Dementia creates significant care needs for people with the disease, their families, and carers. iSupport is a self-help platform developed by the World Health Organization (WHO) to provide education, skills training, and support to dementia carers. It has been adapted in some contexts (Australia, India, the Netherlands, and Portugal). Carers using the existing adapted versions have identified the need to have a more user-friendly version that enables them to identify solutions for immediate problems quickly in real time. The iSupport virtual assistant (iSupport VA) is being developed to address this gap and will be evaluated in a randomized controlled trial (RCT). OBJECTIVE: This paper reports the protocol of a pilot RCT evaluating the iSupport VA. METHODS: Seven versions of iSupport VA will be evaluated in Australia, Indonesia, New Zealand, and Vietnam in a pilot RCT. Feasibility, acceptability, intention to use, and preliminary impact on carer-perceived stress of the iSupport VA intervention will be assessed. RESULTS: This study was funded by the e-ASIA Joint Research Program in November 2020. From January to July 2023, we will enroll 140 dementia carers (20 carers per iSupport VA version) for the pilot RCT. The study has been approved by the Human Research Committee, University of South Australia, Australia (203455). CONCLUSIONS: This protocol outlines how a technologically enhanced version of the WHO iSupport program-the iSupport VA-will be evaluated. The findings from this intervention study will provide evidence on the feasibility and acceptability of the iSupport VA intervention, which will be the basis for conducting a full RCT to assess the effectiveness of the iSupport VA. The study will be an important reference for countries planning to adapt and enhance the WHO iSupport program using digital health solutions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621001452886; https://tinyurl.com/afum5tjz. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/33572.
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.011 |
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".