Using Neurorestorative Approaches for the Rehabilitation of Individuals living with Spinal Cord Injury or Disease
Bibliographic record
Abstract
In recent years, rehabilitation for individuals living with spinal cord injury or disease (SCI/D) has shifted from a focus on compensatory approaches to address loss of function to a focus on neurorestorative approaches. My central thesis aim was to understand the use of neurorestorative approaches in physical rehabilitation for individuals with SCI/D. Regarding the central thesis aim, I identified barriers and facilitators to the use of these approaches. Also, I described the current state of translation and implementation of these neurorestorative approaches as part of Canadian SCI/D rehabilitation. Data were gathered from four main studies of neurorestorative approaches to SCI/D rehabilitation in Canada. Study 1 was a scoping review that investigated the effect of epidural stimulation on different functions for individuals with SCI/D. Study 2 and Study 3 investigated the perspectives of occupational therapists (OTs) and physical therapists (PTs) on activity-based therapy (ABT) using conventional content analysis. Study 2 focused on the inpatient/outpatient hospital setting, while Study 3 looked at the acute care setting. For Study 4, I interviewed OTs and PTs about their use of brain-computer interface-triggered functional electrical stimulation therapy (BCI-FEST). My research identified therapists’ need for increased knowledge and education about neurorestorative approaches to SCI/D rehabilitation using mentorship and large support groups. Implementation of these neurorestorative approaches should occur across the continuum of care yet take into consideration differences between settings and nuances between sites. Across studies, I noted that perspectives from therapists working in rural and remote (i.e., non-specialized SCI/D centres) were not included. Future research should target the perspectives of these individuals as we work towards implementation.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".