Evidence-based medicine curricula and barriers for physicians in training: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To describe the published literature on EBM curricula for physicians in training and barriers during curriculum implementation. METHODS: We performed a systematic search and review of the medical literature on PubMed, Embase, ERIC, Scopus and Web of Science from the earliest available date until September 4, 2019. RESULTS: We screened 9,042 references and included 29 full-text studies and 14 meeting abstracts. Eighteen studies had moderate validity, and 6 had high validity. The EBM curricular structure proved highly variable in between studies. The majority of the EBM curricula was longitudinal with different lengths. Only five studies reported using Kern's six-step approach for curriculum development. Twenty-one articles reported on EBM skills and knowledge, and only 5/29 full-text articles used a validated assessment tool. Time was the main barrier to EBM curriculum implementation. All the included studies and abstracts, independent of the EBM curriculum structure or evaluation method used, found an improvement in the residents' attitudes and/or EBM skills and knowledge. CONCLUSIONS: The current body of literature available to guide educators in EBM curriculum development is enough to constitute a strong scaffold for developing any EBM curriculum. Given the amount of time and resources needed to develop and implement an EBM curriculum, it is very important to follow the curriculum development steps and use validated assessment tools.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".