Qualitative Evaluation of an Online Technology to Support Rural Caregivers of People with Dementia
Bibliographic record
Abstract
Background/Objectives: In rural communities, caregivers of people living with dementia face limited access to support services. Digital interventions offer potential solutions for support. This paper reports on the evaluation of Verily Connect, a web-based multicomponent intervention developed to support caregivers. The aim of this qualitative study was to critically evaluate the implementation of Verily Connect to better understand its barriers and enablers. Methods: Using the Consolidated Framework for Implementation Research (CFIR), qualitative data were collected through semi-structured interviews with 24 health service professionals across 12 rural Australian communities. Thematic analysis was conducted to identify barriers and facilitators to implementation. Results: Key barriers included limited digital literacy, resistance to technology and privacy concerns, as well as competing organisational priorities, and inadequate technological infrastructure. Facilitators included organisational alignment and supportive management. Conclusions: The perceived relevance and usability of Verily Connect were enhanced by its co-design with caregivers and integration into health service models. Addressing digital literacy for caregivers, infrastructure limitations, and organisational readiness is essential for future technology-based health interventions in rural dementia care.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".