LO13: Development of a national, standardized simulation case template
Bibliographic record
Abstract
Innovation Concept: A major barrier to the development of a national simulation case repository and multi-site simulation research is the lack of a standardized national case template. This issue was recently identified as a priority research topic for Canadian simulation based education (SBE) research in emergency medicine (EM). We partnered with the EM Simulation Education Researchers Collaborative (EM-SERC) to develop a national simulation template. Methods: The EM Sim Cases template was chosen as a starting point for the consensus process. We generated feedback on the template using a three-phase modified nominal group technique. Members of the EM-SERC mailing list were consulted, which included 20 EM simulation educators from every Canadian medical school except Northern Ontario School of Medicine and Memorial University. When comments conflicted, the sentiment with more comments in favour was incorporated. Curriculum, Tool or Material: In phase one we sought free-text feedback on the EM Sim Cases template via email. We received 65 comments from 11 respondents. An inductive thematic analysis identified four major themes (formatting, objectives, debriefing, and assessment tools). In phase two we sought free-text feedback on the revised template via email. A second thematic analysis on 40 comments from 12 respondents identified three broad themes (formatting, objectives, and debriefing). In phase three we sought feedback on the penultimate template via focus groups with simulation educators and technologists at multiple Canadian universities. This phase generated 98 specific comments which were grouped according to the section of the template being discussed and used to develop the final template (posted on emsimcases.com). Conclusion: We describe a national consensus-building process which resulted in a simulation case template endorsed by simulation educators from across Canada. This template has the potential to: 1. Reduce the replication of effort across sites by facilitating the sharing of simulation cases. 2. Enable national collaboration on the development of both simulation cases and curricula. 3. Facilitate multi centre simulation-based research by removing confounders related to the local adoption of an unfamiliar case template. This could improve the rigour and validity of these studies by reducing inter-site variability. 4. Increase the validity of any simulation scenarios developed for use in national high-stakes assessment.
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.
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 | 0.088 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".