How are medical students trained to locate biomedical information to practice evidence-based medicine? a review of the 2007–2012 literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.357 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it