Multimodal Cluster Analysis of Medical Residents' Emotions During High‐Fidelity Harassment Bystander Simulation
Bibliographic record
Abstract
ABSTRACT Background High fidelity simulations can be an effective tool for anti‐harassment education. While emotions have been identified as crucial in simulation‐based education, their role in anti‐harassment education within medical training remains underexplored. Objectives We aimed to investigate emotional profiles of medical residents during harassment bystander simulation training via hierarchical clustering based on multimodal emotions data. Methods Twenty seven internal medicine residents with complete data sets that were part of a larger study were recruited. Emotions were captured through self‐report surveys, an electronic bracelet that records electrodermal activity, and speech content analysis based on the residents' simulation debriefing. The study involved residents performing a simulated central line insertion while a simulated harassment took place that they could use to practice intervening in harassment. Results Our cluster analysis revealed three equal‐sized groups: ‘Emotionally Balanced, Minimal Arousal’, ‘Positive, Spiked Arousal’ and ‘Negative High Arousal’. The clusters had distinct levels of self‐report emotions and electrodermal activity. Content analysis revealed distinct emotions, and sources of emotions between the clusters. Post hoc analysis revealed that the ‘Emotionally Balanced, Minimal Arousal’ group showed a higher propensity for directly confronting the harasser, indicating a composed emotional state conducive to focusing on simulation objectives. Conclusions Our findings reveal the varied emotional profiles that can be expected in simulation‐based medical education and underscore the value of a multimodal approach to understanding these dynamics. Furthermore, the study highlights the criticality of recognising the sources of emotions and promoting effective emotion regulation strategies, especially in authentic learning environments where emotional responses are complex and impactful.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".