Knowledge, Attitudes, Beliefs and Behaviors of Physiotherapists to Evidence-Based Practice: A Cross-Sectional Survey
Bibliographic record
Abstract
Introduction: Physiotherapists work as autonomous professionals and in team with other healthcare professionals. The present-day healthcare literature consists of arguments about the importance of outcome research and evidence-based practice. Therefore, studying the currently used and new treatment procedures along with their supporting evidences is of prime importance particularly to the new graduates. Aim: To determine physiotherapists’ self-reported knowledge, attitudes, beliefs and behaviors to evidencebased practice within a university setting. Method: A cross-sectional survey was conducted among postgraduate physiotherapy students (n=75) within the Gujarat University. Participants completed evidence-based practice questionnaire (EBP-Q) designed to determine knowledge, attitudes, beliefs and behaviors, as well as demographic information about themselves and practice settings. Most responses of questionnaire were rated on a 5-point Likert scale, between ‘strongly agree’ and ‘strongly disagree’. Some items included yes/no/do not know responses, whereas others consisted of understand completely/understand somewhat/do not understand responses. Result: Data was analyzed using SPSS version 20.0. Percentage of participants was calculated for responses of knowledge, attitudes, beliefs and behaviors domains in the questionnaire. Conclusion: Physiotherapists have a positive attitude and beliefs about EBP; however, the knowledge and behaviors among them was relatively poor.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".