Effectiveness of online wheelchair mobility and transfer training on the occupational performance in people with spinal cord injuries
Bibliographic record
Abstract
Abstract Background Online training for the rehabilitation of People with Spinal Cord injuries (PwSCI) is necessary. Various environmental barriers create challenges in transferring and transporting these individuals out of the home to participate in face-to-face interventions. Furthermore, these challenges were exacerbated by the COVID-19 pandemic in the past.Study Design: A single-blind randomized controlled trial.Objectives This research aims to investigate the effectiveness of online wheelchair mobility and transfer training, on the level of performance and satisfaction in PwSCI.Setting: SCI associations and hospitals and clinical centers.Methods The PwSCI were randomly divided into an online training group (OTG) and a control group (CG). The OTG received online training during 5 group sessions for five weeks. The results were analyzed to compare changes in occupational performance level and satisfaction after the intervention, and one month later.Results 37 (CG = 18 and OTG = 19) out of 49 participants completed the 5-week intervention and follow-up assessments. The average age of participants in the CG was 35.0 years, and the OTG was 33.7 years. We found a significant increase in performance (p < 0.001) and satisfaction (p < 0.001) within the OTG during the pre-post assessment. There were also significant differences in performance (p < 0.026) and satisfaction (p < 0.015) between groups.Conclusion The results showed that online wheelchair mobility and transfer training can be a suitable method for telerehabilitation and training PwSCI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".