More than a feeling: emotional regulation strategies for simulation-based education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simulation-based education often involves learners or teams attempting to manage situations at the limits of their abilities. As a result, it can elicit emotional reactions in participants. These emotions are not good or bad, they simply are. Their value at any given moment is determined by their utility in meeting the goals of a particular situation. When emotions are particularly intense, or a given emotion is not aligned with the situation, they can impede learners' ability to engage in a simulation activity or debriefing session, as well as their ability to retain knowledge and skills learned during the session. Building on existing guidance for simulation educators seeking to optimize the learning state/readiness in learners, this paper explores the theory and research that underpins the practical application of how to recognize and support learners' emotions during simulation sessions. Specifically, we describe the impact of various emotions on the cognitive processes involved in learning and performance, to inform practical guidance for simulation practitioners: (1) how to recognize and identify emotions experienced by others, (2) how to determine whether those emotional reactions are problematic or helpful for a given situation, and (3) how to mitigate unhelpful emotional reactions and leverage those that are beneficial in achieving the goals of a simulation session.
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.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 it