Clarifying Evidence-Based Medicine in Educational and Therapeutic Experiences of Clinical Faculty Members: A Qualitative Study in Iran
Bibliographic record
Abstract
INTRODUCTION: Although evidence-based medicine has been a significant part of recent research efforts to reform the health care system, it requires an assessment of real life community and patient. The present study strives to clarify the concept of evidence-based medicine in educational and therapeutic experiences of clinical faculty members of Kermanshah University of Medical Sciences (2014). MATERIALS & METHODS: It was a qualitative study of phenomenology. The population consists of 12 clinical faculty members of Kermanshah University Medical Sciences. Sampling was carried out using a purposeful method. Sample volume was determined using adequacy of samples' law. Data gathering occurred through semi-structured interviews. Collaizzi pattern was employed for data interpretation concurrent with data gathering. RESULTS: interpreting the data, three main themes were extracted. They include: 1. Unawareness and disuse (unaware of the concept, disuse, referral to colleagues, experiment prescription) 2. Conscious or unconscious use (using journals and scientific websites, aware of the process). 3. Beliefs (belief or disbelief in necessity). CONCLUSION: It sounds essential to change the behavior of clinical faculty members from passive to active with respect to employing evidence-based medicine as well as to alter negative attitudes into positive ones. In so doing, systematic training program aiming at behavior changing is necessary. Also, providing dissent facilities and infrastructures and removing barriers to the use of EBM can be effective.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".