Balancing Access with Technology: Comparing In-Person and Telerehabilitation Berg Balance Scale Scores among Stroke Survivors
Bibliographic record
Abstract
Purpose: Stroke survivors living in rural and remote communities experience challenges in accessing specialized rehabilitation services. Access to balance assessment after stroke is an essential aspect of the physiotherapy assessment. Telerehabilitation (TRH) can eliminate access disparities; however, adoption into practice has been limited. Our primary objective was to examine agreement between Berg Balance Scale (BBS) scores obtained through TRH and those obtained through traditional in-person assessment of community-dwelling individuals with stroke. Method: Two raters administered the BBS to 20 community-dwelling individuals with stroke, using both TRH and traditional in-person approaches. The order of assessments and rater assignment was randomized. Interrater reliability between the methods was assessed using Krippendorff’s α reliability estimate. A survey was then administered to examine the participants’ perceptions of the two means of assessment. Results: Excellent interrater agreement was found between TRH and in-person assessment ( κ = 0.97; 95% CI: 0.96, 0.99), and responses regarding patients’ perceived hearing and understanding of instructions as well as perceived safety were comparable. In addition, the vast majority of participants agreed or strongly agreed that they would use TRH for future physiotherapy sessions. Conclusions: The results of this study support administration of the BBS using TRH technology; this could improve access to balance assessment for stroke survivors in rural and remote communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".