Volunteers’ Support of Carers of Rural People Living with Dementia to Use a Custom-Built Application
Bibliographic record
Abstract
There is great potential for human-centred technologies to enhance wellbeing for people living with dementia and their carers. The Virtual Dementia Friendly Rural Communities (Verily Connect) project aimed to increase access to information, support, and connection for carers of rural people living with dementia, via a co-designed, integrated website/mobile application (app) and Zoom videoconferencing. Volunteers were recruited and trained to assist the carers to use the Verily Connect app and videoconferencing. The overall research design was a stepped wedge open cohort randomized cluster trial involving 12 rural communities, spanning three states of Australia, with three types of participants: carers of people living with dementia, volunteers, and health/aged services staff. Data collected from volunteers (n = 39) included eight interviews and five focus groups with volunteers, and 75 process memos written by research team members. The data were analyzed using a descriptive evaluation framework and building themes through open coding, inductive reasoning, and code categorization. The volunteers reported that the Verily Connect app was easy to use and they felt they derived benefit from volunteering. The volunteers had less volunteering work than they desired due to low numbers of carer participants; they reported that older rural carers were partly reluctant to join the trial because they eschewed using online technologies, which was the reason for involving volunteers from each local community.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".