Prescribing upper limb exercises after stroke: A survey of current UK therapy practice
Bibliographic record
Abstract
OBJECTIVE: To investigate the current practice of physiotherapists and occupational therapists in prescribing upper limb exercises to people after stroke and to explore differences between professions and work settings. DESIGN: A cross-sectional survey design. PARTICIPANTS: Occupational therapists and physiotherapists working in UK stroke rehabilitation. RESULTS: The survey's response rate was 21.0% (n = 322); with 295 valid responses. Almost two thirds of therapists (64.7%, n = 191) agreed that they always prescribe upper limb exercises to a person with stroke if they can actively elevate their scapula and have grade 1 finger/wrist extension. Most therapists (98.6%, n = 278) prescribed exercises to be completed outside of therapy time, with exercises verbally communicated to family. Standardised upper limb specific outcome measures were used to evaluate the prescribed exercises by 21.9% (n = 62) OF THERAPISTS. DIFFERENCES WERE FOUND BETWEEN PROFESSIONS AND ACROSS WORK SETTINGS. CONCLUSION: The majority of prescribed upper limb exercises were of low intensity (range of motion or stretching exercises) rather than repetitive practice or strengthening exercises. The use of standardised outcome measures was low. Progression of exercises and the provision of written instructions on discharge occur less frequently in inpatient settings than outpatient and community settings.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".