MP27: Using a massive online needs assessment to guide the evolution of the EM Sim Cases website
Bibliographic record
Abstract
Innovation Concept: EM Sim Cases is an innovative, open-access website that was created in 2015 to publish medical simulation resources including standardized, peer-reviewed simulation cases. Herein we describe our interim analysis. Methods: We performed a massive online needs assessment using a methodology previously described by Chan et. al. to determine how we can shape EM Sim Cases to meet the needs of learners and educators who use it. We engaged with simulation experts from the Emergency Medicine Simulation Education Research Collaborative to design a Google Forms survey using best practices in survey design. We distributed the survey to our target community of practice via Twitter, email, and a blog post published on emsimcases.com. Curriculum, Tool, or Material: We received 81 responses from simulation educators representing 8 medical specialties and 13 countries. Most survey respondents identified themselves as staff physicians (n = 44) and specialized in emergency medicine (n = 39). They had 0-21+ years of experience. 37% of respondents (n = 30) stated that material from EM Sim Cases makes up 25% or more of their simulation curriculum. Several respondents noted that using this content made them feel more confident and more current. Respondents praised EM Sim Cases for a well-organized case format, the proper level of detail, consistency between case designs, and the wide variety of cases. Suggested improvements included an opportunity to directly comment on cases and more cases in pediatric, rural, and advanced airway management situations. Suggestions were made to improve the navigability of the website. Respondents wanted to see additional blog content on debriefing strategies and self-made task/skill trainers. Conclusion: EM Sim Cases is a novel, free open-access simulation resource. Using a massive online needs assessment we were able to determine future directions including case topics, website reorganization, and educational material. We were also able to capture how impactful a resource like this can be to clinical and educational practice outside of the simulation setting.
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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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".