The Future of Emergency Medicine (EM) Sim Cases: A Modified Massive Online Needs Assessment
Bibliographic record
Abstract
Objective Emergency Medicine (EM) Sim Cases was initially developed in 2015 as a free open-access simulation resource. To ensure the future of EM Sim Cases remains relevant and up to date, we performed a needs assessment to better define our audience and facilitate long-term goals. Methods We delivered a survey using a modified massive-online-needs-assessment methodology through an iterative process with simulation experts from the EM Simulation Educators Research Collaborative. We distributed the survey via email and Twitter and analyzed the data using descriptive statistics and thematic analysis. Results We obtained 106 responses. EM Sim Cases is commonly used by physicians primarily as an educational resource for postgraduate level trainees. Perceived needs included resuscitation, pediatrics, trauma, and toxicology content. Prompted needs included non-simulation-case educational resources, increased case database, and improved website organization. Conclusions Data collected from our needs assessment has defined our audience allowing us to design our long-term goals and strategies.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".