Learning by viewing versus learning by doing: A comparative study of observer and participant experiences during an interprofessional simulation training
Bibliographic record
Abstract
Larger student groups and pressure on limited faculty time have raised the question of the learning value of merely observing simulation training in emergency medicine, instead of active team participation. The purpose of this study was to examine observers and hands-on participants' self-reported learning outcomes during simulation-based interprofessional team training regarding non-technical skills. In addition, we compared the learning outcomes for different professions and investigated team performance relative to the number of simulations in which they participated. A concurrent mixed-method design was chosen to evaluate the study, using questionnaires, observations, and focus group interviews. Participants included a total of 262 postgraduate and bachelor nursing students and medical students, organised into 44 interprofessional teams. The quantitative data showed that observers and participants had similar results in three of six predefined learning outcomes. The qualitative data emphasised the importance of participating in different roles, training several times, and training interprofessionally to enhance realism. Observing simulation training can be a valuable learning experience, but the students' preferred hands-on participation and learning by doing. For this reason, one can legitimise the observer role, given the large student groups and limited faculty time, as long as the students are also given some opportunity for hands-on participation in order to become more confident in their professional roles.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".