Parkwood's VIP4SCI platform: A virtual e-health self-management solution for persons with spinal cord injury across the care continuum
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".