Evidence-based-practice profile among physiotherapists: a cross-sectional survey in France
Bibliographic record
Abstract
To establish an inventory of the Evidence-Based Practice (EBP) among physiotherapists (PT) in France assessing EBP knowledge, perception, and utilisation in practice. A cross-sectional survey was performed using a French translation of the EBP2 questionnaire including the 5 EBP domains (Relevance, Terminology, Confidence, Practice and Sympathy). Participants completed the online survey from February 26th to August 31st, 2021. Scores were summarised by EBP domain for each participant. Pairwise Pearson’s correlations between domains scores and a hierarchical clustering on principal component analysis were conducted. In total, 542 participants were included in the analysis. Majority of participants were male with median age of 30 years (IQR: 26–36 years). Regarding EBP domain scores, PT perceived relevance of EBP but had difficulty to apply it in practice. Furthermore, positive correlations exist between the 5 domains. Participants were divided into three clusters. Those reporting poor scientific analysis skills and high barriers were older, less graduated, less trained in EBP and graduated earlier. French PT perceived research to be important in their current practice and had a favourable opinion of EBP. However, they reported a lack of confidence, difficulty to understand terminology and to use it in practice. These findings and their profiles may help to improve PT’s EBP education in France.
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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 | 0.003 |
| 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 | 0.000 |
| 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 | 0.000 |
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".