Development of a Path to Home Mobile App for the Geriatric Rehabilitation Program at Bruyère Continuing Care: Protocol for User-Centered Design and Feasibility Testing Studies
Bibliographic record
Abstract
BACKGROUND: As the population ages, the need for appropriate geriatric rehabilitation services will also increase. Pressures faced by hospitals to reduce length of stay and reduce costs have driven the need for more complex care being delivered in the home or community setting. As a result, a multifaceted approach that can provide geriatric rehabilitation patients with safe and effective person- and family-centered care during transitions from hospital to home is required. We hypothesize that a technology-supported person- and family-centered care transition could empower geriatric rehabilitation patients, engage them in shared decision making, and ultimately help them to safely manage their personalized needs during care transitions from hospital to home. OBJECTIVE: The purpose of this study is to design and test the feasibility of a novel Path to Home mobile app to manage the personalized needs of geriatric rehabilitation patients during their transitions from hospital to home. METHODS: This study will consist of (1) codesigning a patient- and provider-tailored mobile app, and (2) feasibility pilot testing of the mobile app to manage the needs of geriatric rehabilitation patients when leaving the hospital. In phase 1, we will follow a user-centered design process integrated with a modern agile software development methodology to iteratively codesign the personalized care transition Path to Home mobile app. In phase 2, we will conduct a single-arm feasibility pilot test with geriatric rehabilitation patients using the personalized care transition Path to Home mobile app to manage their needs during the transition from hospital to home. RESULTS: The project was funded in May 2018, and enrollment and data analysis are underway. First results are expected to be submitted for publication in 2019. CONCLUSIONS: Our findings will help validate the use of this technology for geriatric rehabilitation patients discharged from the hospital to home. Future research will more rigorously evaluate the health and economic benefits to inform wide-scale adoption of the technology. REGISTERED REPORT IDENTIFIER: RR1-10.2196/11031.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".