Stakeholders’ perspective on the development of a virtual clinic for patients with spinal cord injury: a qualitative study
Bibliographic record
Abstract
PURPOSE: This study aims to explore the priorities, preferences, and feedback of multiple healthcare professionals to inform the future development of virtual clinics for community-dwelling adults with spinal cord injury (SCI) in Ontario, Canada. METHODS: Interpretive description methodology was used to guide our exploration. Semi-structured interviews were conducted with 15 expert healthcare professionals (HCPs) involved in the care of patients with SCI. Interviews were recorded and transcribed verbatim. Interview transcripts were then analyzed using a six-phase thematic analysis approach. RESULTS: HCPs perceived virtual care to improve access to care over the long term, particularly to those living in rural areas, as well as increase connections between different providers. However, participants highlighted that in-person care is still required for management of severe SCI-related sequelae that can be life-threatening, such as pressure ulcers, spasticity, respiratory issues, and bowel and bladder complications. CONCLUSION: Our findings can be used to inform policymakers, HCPs, and stakeholders involved with SCI rehabilitation when establishing a virtual clinic for patients with SCI. Results of this study found that policymakers and HCPs should consider hybridized (blend of virtual and in-person) healthcare and uptake of multidisciplinary approaches within the virtual healthcare systems.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".