Knowledge, Attitude and Implementation of Evidence-Based Practice among Physiotherapists Working in the Kingdom of Saudi Arabia: A Cross-Sectional Survey
Bibliographic record
Abstract
The current study aimed to investigate knowledge, attitudes, and implementation of evidence-based practice among physiotherapists working in Saudi Arabia. A sample of physiotherapists working in various outpatient settings in Saudi Arabia participated in this survey. Sixty-four therapists (30 females, 34 males) completed a 28-item survey questionnaire. Approximately half of the participants indicated that evidence-based practice was useful and important for clinical practice. About 60% of the participants reported that they have adopted the evidence-based practice on a regular basis. Participants who had a membership in a physiotherapy organization and had advanced degrees showed more positive attitudes (t = −2.31, p = 0.02 and t = −2.15, p = 0.04, respectively) and greater levels of knowledge (t = −2.32, p = 0.02 and t = −3.86, p = 0.001, respectively) regarding evidence-based practice terminology. Furthermore, place of training (trained overseas) was associated with a positive attitude towards literature findings (t = 2.26, p = 0.03). The results of this study demonstrated that participants reported that evidence-based practice had not been extensively implemented, despite positive attitudes regarding its implementation among physiotherapists practicing in Saudi Arabia.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 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".