Knowledge and Attitude Toward Evidence-Based Medicine and Associated Factors Among Medical Interns in Amhara Regional State Teaching Hospitals, Northwest Ethiopia: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Evidence-based medicine (EBM) is widely accepted in medicine. It is necessary to improve the knowledge and attitudes of medical students in the use of evidence. In Ethiopia, little is known about medical students' knowledge and attitudes toward EBM. OBJECTIVE: This study aimed to assess the knowledge and attitudes toward EBM and its associated factors among medical interns in teaching hospitals. METHODS: A cross-sectional survey was conducted using a random sample of medical interns in teaching hospitals in Ethiopia. Multivariable logistic regression analyses were used to identify the factors associated with the knowledge and attitudes toward EBM. Adjusted odds ratio (AOR) with 95% confidence interval and P≤.05 was used to quantify strength of association between variables. RESULTS: Out of a sample of 423 medical interns, 403 completed the questionnaire (95.3% response rate). Overall, 68.0% (274/403 of respondents had a favorable attitude toward EBM and 57.1% (230/403) had good knowledge of EBM. The majority (355/403, 88.1%) of participants had internet access. Only 19.6% (79/403) of respondents had received EBM-related training. Respondents' knowledge of EBM was associated with previous EBM training (AOR 2.947, 95% CI 1.648-5.268, P<.001), understanding of sensitivity (AOR 2.836, 95% CI 1.824-4.408, P=.003), and internet access (AOR 2.914, 95% CI 1.494-5.685, P=.002). The use of an electronic database as a source of information (AOR 1.808, 95% CI 1.143-2.861, P=.01) and understanding of absolute risk reduction (AOR 2.750, 95% CI 1.105-6.841, P=.03) were predictors of positive attitudes. CONCLUSIONS: This study demonstrates a lack of formal EBM training and awareness of basic concepts of EBM among medical interns. Medical intern attitudes toward EBM are relatively good. To enhance EBM knowledge and skills, formal teaching of EBM should be integrated into medical education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".