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Record W3045684022 · doi:10.2196/18169

MyPath to Home Web-Based Application for the Geriatric Rehabilitation Program at Bruyère Continuing Care: User-Centered Design and Feasibility Testing Study

2020· article· en· W3045684022 on OpenAlexaffvenue
Chantal Backman, Anne Harley, Craig Kuziemsky, Jay Mercer, Liam Peyton

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMacEwan UniversityOttawa HospitalBruyèreUniversity of Ottawa
Fundersnot available
KeywordsRehabilitationMedicineTest (biology)User-centered designAgile software developmentGeriatricsWeb applicationNursingPhysical therapyComputer scienceWorld Wide WebHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: When older adults return home from geriatric rehabilitation in a hospital, remembering the plethora of medical advice and medical instructions provided can be overwhelming for them and for their caregivers. OBJECTIVE: The overall objective was to develop and test the feasibility of a novel web-based application called MyPath to Home that can be used to manage the personalized needs of geriatric rehabilitation patients during their transition from the hospital to home. METHODS: This study involved (1) co-designing a patient- and clinician-tailored web-based application and (2) testing the feasibility of the application to manage the needs of geriatric rehabilitation patients when leaving the hospital. In phase 1, we followed a user-centered design process integrated with the modern agile software development methodology to iteratively co-design the application. The approach consisted of three cycles in which we engaged patients, caregivers, and clinicians to design a series of prototypes (cycles 1-3). In phase 2, we conducted a single-arm feasibility pilot test of MyPath to Home. Baseline and follow-up surveys, as well as select semistructured interviews were conducted. RESULTS: In phase 1, semistructured interviews and talk-aloud sessions were conducted with patients/caregivers (n=5) and clinicians (n=17) to design the application. In phase 2, patients (n=30), caregivers (n=18), and clinicians (n=20) received access to use the application. Patients and their caregivers were asked to complete baseline and follow-up surveys. A total of 91% (21/23) of patients would recommend this application to other patients. In addition, clinicians (n=6) and patients/caregivers (n=6) were interviewed to obtain further details on the value of the web-based application with respect to engaging patients and facilitating communication and sharing of information with the health care team. CONCLUSIONS: We were successful at designing the MyPath to Home prototype for patients and their caregivers to engage with their clinicians during the transition from geriatric rehabilitation to home. Further work is needed to increase the uptake and usage by clinicians, and determine if this translates to meaningful changes in clinical and functional outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11031.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.094
GPT teacher head0.425
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2020
Admission routes2
Has abstractyes

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