Exploring the use of ultrasound imaging by physiotherapists: An international survey
Bibliographic record
Abstract
BACKGROUND: National surveys in New Zealand, Australia and the United Kingdom suggest ultrasound imaging (USI) use by physiotherapists is increasing. However, concerns exist regarding clarity for scopes of practice, and availability and standardisation of training. OBJECTIVES: To investigate physiotherapists' understanding of scopes of practice for the use of USI; clarify the professional contexts, clinical uses and levels of training; and identify barriers preventing physiotherapists' USI use. DESIGN: A cross-sectional, observational survey. METHODS: An Internet-based survey, offered in 20 different languages, was used including items covering five domains: (1) demographic and professional characteristics; (2) knowledge of scope of practice; (3) USI use; (4) USI training content and duration; and (5) perceived barriers to physiotherapists' use of USI. RESULTS: 1307 registered physiotherapists from 49 countries responded; 30% were unsure of the scope of practice for physiotherapists' USI use. 38% of participants were users of USI, reporting varied contexts and clinical uses, reflected in the broader categories of: (i) biofeedback; (ii) diagnosis; (iii) assessment; (iv) injection guidance; (v) research; (vi) and teaching. The training users received varied, with formal training more comprehensive. 62% were non-users, the most common barrier was lack of training (76%). CONCLUSION: These findings suggest physiotherapists' USI use is increasing in various contexts; however, there is uncertainty regarding scopes of practice. There are discrepancies in training offered, with a lack of training the most common barrier to physiotherapists' use of USI. International guidelines, including a USI training framework, are needed to support the consistent and sustainable use of USI in physiotherapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".