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Record W4401825124 · doi:10.1177/20552076241272618

Parkwood's VIP4SCI platform: A virtual e-health self-management solution for persons with spinal cord injury across the care continuum

2024· article· en· W4401825124 on OpenAlexaffabout
Luxshmi Nageswaran, Charlie Giurleo, Merna Seliman, Heather Askes, Zeina Abu-Jurji, B. Catharine Craven, Anna Kras‐Dupuis, Julie Watson, Dalton L. Wolfe

Bibliographic record

VenueDigital Health · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsSpinal Cord Injury OntarioToronto Rehabilitation InstituteSt Joseph's Health CareUniversity Health NetworkMcMaster UniversityParkwood InstituteWestern University
FundersCraig H. Neilsen Foundation
KeywordsUsabilityPsychological interventionFocus groupSpinal cord injuryRehabilitationHealth careMedicineAnalyticsNursingPsychologyPhysical therapyComputer scienceSpinal cord

Abstract

fetched live from OpenAlex

Objective: Parkwood VIP4SCI platform is a virtual e-health solution adapted from a version created for Spinal Cord Injury Ontario (SCIO) that focused on self-management skill development for persons with spinal cord injury (SCI) transitioning between stages of care, in partnership with caregivers and clinicians. This evaluation of the platform informs the usability and feasibility of a model to facilitate service care aims postrehabilitation. Design: Inpatients were randomized into two groups (Platform or Standard Care (i.e., delayed access)). Outpatients were given access at enrollment. Pre-post assessments were completed using surveys, and platform analytics were collected. Weekly check-ins were introduced to increase engagement. Focus groups were held with a subset of participants near study completion. Results: VIP4SCI was viewed as usable and feasible. Platform satisfaction assessed on a -3 to +3 scale ranged from +0.9 to 2.5, demonstrating positive agreement. Self-efficacy related to self-management ranged from 5.4 to 7.6 out of 10. The educational resource hub was identified as the most beneficial feature. Lack of clinician uptake was a barrier to integration into day-to-day practice. Conclusions: Platform usage was low among all groups despite the perceived need for facilitating care coordination with consistent and intentional self-management programming. Despite the lack of uptake, partly due to challenges associated with the pandemic, conclusions on platform features and barriers to implementation will help to inform future programming.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.045
GPT teacher head0.422
Teacher spread0.377 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2024
Admission routes2
Has abstractyes

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