Hidden identity simulation: Using secret character roles to practice navigating event medical team dynamics
Bibliographic record
Abstract
What was the educational challenge? Interdisciplinary medical teams often collaborate for the first time at mass-gathering events. Simulation helps participants navigate interpersonal dynamics. A gamified simulation could offer an engaging, low-stakes method to introduce emotionally charged topics like team conflict.What was the solution? Hidden Identity Simulation (HID SIM) is a gamified simulation where participants with randomly assigned secret character roles (e.g. ‘The Know-It-All’) navigate diverse personalities while managing a case. A debrief highlights team dynamic management strategies.How was the solution implemented? A serotonin toxicity case was piloted using HID SIM in a simulation theatre. Twenty-nine participants randomly selected character cards that outline personality descriptions and conditions that modify character intensity.What lessons were learned that are relevant to a wider global audience? Participants responded positively, rating HID SIM 4.75/5 overall and 4.43/5 for engagement (n = 28). ‘Less stressful than anticipated’ scored 3.54/5 (n = 28), and ‘less stressful than other simulations’ was rated 3.68/5 (n = 19). Participants rated their pre-/post-simulation self-efficacy in several teamwork and medical competencies. All competencies showed statistically significant increases (p < 0.001) post-simulation.What are the next steps? Future steps include writing new character cards, expanding HID SIM to other clinical settings, and designing customized decks targeting specific team dynamics.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".