Evaluating Emotional Outcomes of Medical Students in Pediatric Emergency Medicine Telesimulation
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has challenged the feasibility of traditional in-person simulation-based clinical training due to the public health recommendation on social distancing. During the pandemic, telesimulation training was implemented to avoid multiple students and faculties gathering in confined spaces. While medical trainees' perceived emotions have been acknowledged as a critical outcome of the in-person simulation-based training, the impact of telesimulation on trainees' emotions has been unexamined. We conducted an educational team-based simulation study with a pediatric case of septic shock. Seventeen and twenty-four medical students participated in the telesimulation training and in-person simulation training, respectively. The institutional pandemic social restrictions at the time of each training session determined the participant assignment to either the telesimulation training or in-person simulation training. All participants responded to the Japanese version of the Medical Emotion Scale, which includes 20 items rated on a five-point Likert-type scale before, during, and after the simulation sessions. The measured emotions were categized into four emotion groups according to two dimensions: positive or negative and activating or deactivating emotions. The one-way analysis of variance between the telesimulation and in-person simulation training revealed no significant differences in the emotions perceived by the participants before, during, and after the simulation training sessions. The perceived emotions of medical students were comparable between the telesimulation and in-person simulation training. Further longitudinal studies with larger samples and multiple variables are needed to generalize the effectiveness of telesimulation.
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".