Bibliographic record
Abstract
INTRODUCTION: Physicians are expected to educate patients, students, colleagues, as well as members of the interdisciplinary team. However, unlike in the United Kingdom and Canada, US medical schools do not require teaching as a competency for graduation. High-quality teaching and communication skills are necessary to ensure patient safety and trust. As the field of medicine becomes increasingly expansive and complex, it can no longer rely on empiric forms of knowledge exchange. Physician training must include pedagogy to create competent educators. In this project, the educational efforts of senior medical students providing simulation-based education to junior students were compared with that provided by faculty-led education. METHODS: The "Medical Students as Simulation Educators" (MSASE) course consists of a didactic and a teaching application component. The didactic component delivers learning modules on topics such as simulation education history, learning theories, debriefing and feedback, curriculum development, teaching methods, assessment, evaluation, and essentials of running the Laerdal SimMan 3G simulation system. After completing didactic courses, student educators apply their pedagogic skills in facilitator-guided, high-fidelity clinical simulations in areas such as anaphylaxis, heart failure, and atrial fibrillation. The evaluation of MSASE compared the knowledge gain of learners led by clinical faculty to those led by student-educators. Learners completed the same multiple-choice quiz (MCQ) before and after the simulation training. In addition, student satisfaction surveys were used to assess their attitudes to being taught by their peers. RESULTS: Scores from the pre- and post-tests were nearly identical in the faculty and student-led groups. The faculty-led group's average pre- and post-activity scores were 41.67% and 72.81%, respectively. The average increase in scores was 31.14%. The student-led group's average pre- and post-activity scores were 44.43% and 75.71%, respectively. The average increase in scores was 31.28%. The results of the student satisfaction survey were supportive of peers as educators. The survey used a 5-point scale with 1 representing "strongly disagree" and 5 representing "strongly agree". Aspects surveyed include learning objective identification and achievement, educator preparedness, organization and structure, simulation realism, complexity appropriateness, engagement level, quality of debriefing, and more. The average student satisfaction score for each aspect of the survey was greater than 4.5. The average of all elements surveyed was 4.75/5. CONCLUSIONS: Data support the contention that medical students are equally effective in simulation-based teaching as clinical faculty. Participating student educators and student learners reported satisfaction with the MSASE experience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".