Rehabilitation clinicians’ perspectives of reactive balance training
Bibliographic record
Abstract
ABSTRACT Purpose Reactive balance training (RBT) aims to improve reactive balance control. However, because RBT involves clients losing balance, clinicians may view that it is unsafe or not feasible for some clients. We aimed to explore how clinicians are specifically implementing RBT to treat balance and mobility issues. Materials and methods Physiotherapists and kinesiologists across Canada who reported that they include RBT in their practices were invited to complete telephone interviews about their experience with RBT. Interviews were transcribed verbatim, and analysed using a deductive thematic analysis. Results Ten participants completed telephone interviews, which lasted between 30-60 minutes. Participants were primarily working in a hospital setting (inpatient rehabilitation (n=3); outpatient rehabilitation (n=2), and were treating those with neurological disorders (n=5). Four main themes were identified: 1) there is variability in RBT approaches; 2) knowledge can be a barrier and facilitator to RBT; 3) reactive balance control is viewed as an advanced skill; and 4) RBT experience builds confidence. Conclusions Our findings suggest a need for resources to make clinical implementation of RBT more feasible.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".