What Are the Barriers to Residentsʼ Practicing Evidence-Based Medicine? A Systematic Review
Bibliographic record
Abstract
PURPOSE: Insufficient time and lack of skills are important barriers to the practice of evidence-based medicine (EBM). Residents could have additional barriers because their practice can be strongly influenced by the educational system and clinical supervisors. The purpose of this study, therefore, was to systematically appraise and summarize the literature on the barriers that residents experience in the application of EBM in daily practice. METHOD: The authors searched MEDLINE, EMBASE, the Cochrane Library, CINAHL, and ERIC for publications preceding January 2008. Additionally, they manually screened the abstracts of relevant conferences (Association for Medical Education in Europe, Society of General Internal Medicine, Society of Medical Decision Making, Ottawa, and Evidence-Based Health Care Teachers & Developers) from January 2001 until January 2008. The search was extended by contacting experts in the field. Original studies on barriers to applying EBM in daily practice were included. Methodological quality was assessed and results were extracted by two reviewers using prespecified forms. RESULTS: The search resulted in 511 titles, 84 abstracts, and 3 studies suggested by experts, of which 9 were included in this review. The quality of the included studies was high. The most frequently mentioned barriers for residents were limited available time (28%-85%), attitude, and knowledge and skills. In four studies, specific barriers related to the position of residents, such as influences from staff members, lack of experience in EBM, and low possibilities to change conditions, were described. CONCLUSIONS: Residents experience specific barriers to practice EBM. These barriers should be recognized and integrated into EBM training programs for residents.
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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 | 0.039 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".