EVALUATION OF SERVICE NAVIGATION AND NETWORKING FOR DEMENTIA CARE IN RURAL COMMUNITIES (SENDER) APP
Bibliographic record
Abstract
Identifying and locating appropriate services within a fragmented health systems can be challenging and frustrating due to lack of knowledge on local services (Gorska et al). Rural communities are particularly impacted over long distance travel to access services (Umstattd et al). A smartphone app has the potential to ease service navigation and connect dementia care givers and providers with each other. Using a co-design and co-production approach, we worked and evaluated the feasibility, acceptability and impact of a service navigation and networking app “SENDER”, with 24 care givers and providers of people with dementia with smartphones in rural Victoria, Australia. We collated, mapped and uploaded data on local health, community and social services into the app. Through usage monitoring, focus groups, and a survey, we examined how often the SENDER app was used, how it changed the care givers’ knowledge of dementia services and support networks, how it affected their social connectedness to other dementia service users and providers, how its use affected care givers’ burden, and how it affected service use. Feedback was also obtained to explore the app’s ease of use. The networking function enabled rural dementia service providers and care givers to share information about transportation options to services, what the journey was like, and important amenities during travel and nearby the service. By involving care givers and providers in the app design and beta-testing, we add research knowledge about how to capitalise on revolutions in technology and best use technology to assist 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".