Group-based circuit training to improve mobility after stroke: a cross-sectional survey of German and Austrian physical therapists in outpatient settings
Bibliographic record
Abstract
BACKGROUND: A contextual transferability analysis identified group-based circuit training (GCT) as an optimal intervention in German and Austrian outpatient physical therapy to improve mobility post-stroke. GCT incorporates task-oriented, high-repetitive, balance, aerobic and strength training and allows for increased therapy time without increasing personnel. OBJECTIVE: To determine the extent to which German and Austrian physical therapists (PTs) use GCT and its components in the outpatient treatment of stroke-related mobility deficits and to identify factors associated with using GCT components. METHODS: A cross-sectional online survey was conducted. Data were analyzed descriptively and using ordinal regression. RESULTS: Ninety-three PTs participated. None reported using GCT moderately to frequently (4-10/10 patients). The percentage of PTs reporting frequent use (7-10/10 patients) of task-oriented, balance, strength, aerobic, and high-repetitive training was 45.2%, 43.0%, 26.9%, 19.4%, and 8.6%, respectively. Teaching or supervising students, time for evidence-based practice activities at work, and working in Austria was associated with using GCT components frequently. CONCLUSION: German and Austrian PTs do not yet use GCT in outpatient physical therapy for stroke. Almost half of PTs, however, employ task-oriented training as recommended across guidelines. A detailed, theory-driven and country-specific evaluation of barriers to GCT uptake is necessary to inform 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".