A Web-Based Self-management App for Living Well With Dementia: User-Centered Development Study
Bibliographic record
Abstract
BACKGROUND: Self-management, autonomy, and quality of life are key constructs in enabling people to live well with dementia. This population often becomes isolated following diagnosis, but it is important for them to feel encouraged to maintain their daily activities and stay socially active. Promoting Independence in Dementia (PRIDE) fosters social inclusion and greater dementia self-management through an interactive handbook. OBJECTIVE: This study aimed to develop a paper-based PRIDE manual on a web-based platform. METHODS: Two overarching stages were used to create the web-based version of PRIDE. The first was Preliminary Development, which encompassed tendering, preliminary development work, consultations, beta version of the website, user testing and consultation on beta version, and production of the final web-based prototype. The second stage was Development of the Final PRIDE App, which included 2 sprints and further user testing. RESULTS: Through a lengthy development process, modifications were made to app areas such as the log-in process, content layout, and aesthetic appearance. Feedback from the target population was incorporated into the process to achieve a dementia-friendly product. The finished PRIDE app has defined areas for reading dementia-related topics, creating activity plans, and logging these completed activities. CONCLUSIONS: The PRIDE app has evolved from its initial prototype into a more dementia-friendly and usable program that is suitable for further testing. The finished version will be tested in a reach, effectiveness, adoption, implementation, and maintenance study, with its potential reach, effectiveness, and adoption explored. Feedback gathered during the reach, effectiveness, adoption, implementation, and maintenance study will lead to any further developments in the app to increase its applicability to the target audience and usability.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".