Evidence‐based Medicine Simulation: A Novel and Practice‐relevant Approach to Teaching Real‐time Literature Searching to Emergency Medicine Residents
Bibliographic record
Abstract
INTRODUCTION: Evidence-based medicine (EBM) and literature searching skills are competencies within the emergency medicine (EM) residency curriculum. Previously in our residency program, a librarian taught literature searching instruction, including a classroom-based overview of search engines. Learners reported low engagement and poor retention. To improve engagement, interest, and skill retention, we used a novel approach: simulation to teach real-time literature searching. METHODS: Based on a needs assessment of our EM residents, we created a literature searching workshop using a flipped classroom approach and high-fidelity simulation. Goals of the session were to be interactive, engaging, and practice-relevant. With a librarian, we developed a brief list of EM-relevant databases, including tips for searching and links to sites/apps. Prereadings also covered the hierarchy of evidence and formulating a good clinical (PICO) question. Residents (12 junior residents) participated in a high-fidelity simulation involving a stable patient whose management required a literature search to inform decisions. Feedback was collected on the simulation experience. RESULTS: Residents received the list of EM-relevant databases 7 days prior and were instructed to set up and test the resources on their smartphones. The day of the session, one resident volunteered to lead the simulation; all residents participated in the search on their smart phones. Collectively, it took 4.5 minutes to find a study that adequately addressed the clinical question and to manage the patient accordingly. Feedback on the simulation was positive. Students found it "very real and practical" and "immediately institutable into practice." It helped residents learn to efficiently and effectively search the literature while managing a stable patient. CONCLUSION: A flipped-classroom simulation-based teaching strategy made learning literature searching more interesting, engaging, and applicable to EM practice. Based on popular demand, we will continue to use this teaching method.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".