How are medical students trained to locate biomedical information to practice evidence-based medicine? a review of the 2007–2012 literature
Bibliographic record
Abstract
OBJECTIVES: This study describes how information retrieval skills are taught in evidence-based medicine (EBM) at the undergraduate medical education (UGME) level. METHODS: The authors systematically searched MEDLINE, Scopus, Educational Resource Information Center, Web of Science, and Evidence-Based Medicine Reviews for English-language articles published between 2007 and 2012 describing information retrieval training to support EBM. Data on learning environment, frequency of training, learner characteristics, resources and information skills taught, teaching modalities, and instructor roles were compiled and analyzed. RESULTS: Twelve studies were identified for analysis. Studies were set in the United States (9), Australia (1), the Czech Republic (1), and Iran (1). Most trainings (7) featured multiple sessions with trainings offered to preclinical students (5) and clinical students (6). A single study described a longitudinal training experience. A variety of information resources were introduced, including PubMed, DynaMed, UpToDate, and AccessMedicine. The majority of the interventions (10) were classified as interactive teaching sessions in classroom settings. Librarians played major and collaborative roles with physicians in teaching and designing training. Unfortunately, few studies provided details of information skills activities or evaluations, making them difficult to evaluate and replicate. CONCLUSIONS: This study reviewed the literature and characterized how EBM search skills are taught in UGME. Details are provided on learning environment, frequency of training, level of learners, resources and skills trained, and instructor roles. IMPLICATIONS: The results suggest a number of steps that librarians can take to improve information skills training including using a longitudinal approach, integrating consumer health resources, and developing robust assessments.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".