Development of a Tablet-Based Outpatient Care Application for People With Dementia: Interview and Workshop Study
Bibliographic record
Abstract
Background: Dementia management presents a significant challenge for individuals affected by dementia, as well as their families, caregivers, and health care providers. Digital applications may support those living with dementia; however only a few dementia-friendly applications exist. Objective: This paper emphasizes the necessity of considering multiple perspectives to ensure the high-quality development of supportive health care applications. The findings underscore the importance of incorporating input from stakeholders and the needs of affected families into application development. Methods: A qualitative approach was chosen, consisting of three interviews and an expert workshop. The interviews and the workshop were recorded and transcribed, and qualitative content analysis was carried out according to the methodology described by Kuckartz with the support of MAXQDA. Results: During the development phases of the application, team meetings and discussions took place. We found that general practitioners and family caregivers play pivotal roles in the treatment and care of people with dementia, often expressing specific preferences and suggestions regarding supportive and assistive technologies. Moreover, the successful development of a useful tablet application requires robust scientific and multidisciplinary discussions and teamwork within the health care community. Conclusions: This paper underscores the necessity of including multiple scientific, clinical, and technical perspectives to ensure the high-quality development of supportive health care applications. Furthermore, adopting a spiral development approach inclusive of feedback loops is imperative for iterative refinement and enhancement of the application.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".