Fall Prevention and Management App Prototype for the Elderly and Their Caregivers
Bibliographic record
Abstract
The objective of this article is to develop a validated mobile app prototype to empower the elderly and caregivers to manage falls that provides personalized and actionable educational materials at the point of care and improves the engagement of the elderly and caregiver in adopting validated fall management practices; To determine the usefulness and suitability of a fall management mobile app to the elderly and caregivers. The method used is a knowledge management approach is used to implement the app based on 2 validated models: Patient Health Engagement Model and Rockwood frailty index. A mixed method evaluation including a cognitive walk through is used to collect end-user feedback from the elderly and caregivers, on the usability, usefulness, and suitability of the app. The app was deemed easy to use, informative and understandable. Potential improvement areas include: larger print; less wordy interfaces; better navigation features; data sharing functionalities; and voice readers. These suggestions will be incorporated in the future. The conclusion of this article is that smartphones have vast potential in providing relevant and creditable fall management information to elderly and caregivers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".