Medical trainees' emotions and their effects on perceptions of performance and team mood in team‐based simulations
Bibliographic record
Abstract
BACKGROUND: Emotions affect performance in learning contexts; however, their effects on medical trainees' performance in highly ecologically valid settings, like team-based simulation training, are not well understood. It is therefore imperative to know which emotions are experienced by medical trainees and the impacts of these emotions on perceptions of performance and team mood. AIMS: To extend the understanding of medical trainees' emotions in the context of team-based medical simulations using a new self-report tool (Situated Emotion Regulation Questionnaire; SERQ). SAMPLE: Participants were 106 medical trainees participating in team-based simulations. Seventy-one participated in multiple simulations. METHODS: A field-based, mixed-methods methodology was used. Medical trainees self-reported their emotions and perceptions of individual performance, team performance and team mood. Multi-level analyses were used to account for nestedness. Debriefings were qualitatively analysed to provide validity evidence for the SERQ. RESULTS: Team leaders reported significantly higher levels of shame post-simulation than team members. A variable comprising post-simulation happiness and hopefulness was a significant predictor of perceptions of team performance and team mood. Post-simulation frustration was a significant predictor of perceptions of team mood. Participants' SERQ responses demonstrated alignment or mixed alignment with their debriefing responses. CONCLUSION: Using multi-level analyses, our research provides insight into medical trainees' emotions and their effects on perceptions in highly ecologically valid simulation trainings. Future medical education training may use these findings to develop curricula and simulations to induce specific emotions or practice emotion regulation. Additionally, the SERQ demonstrated promising validity evidence and may be a valuable future research and educational tool.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".