Experiences of Participants During In Situ Simulation With a Learner Present
Bibliographic record
Abstract
BACKGROUND: While in situ simulation (ISS) provides robust value for health care teams, it is less clear how medical learners affect the experiences of other participants. METHODS: This was a single-center qualitative analysis of a community hospital's emergency department ISS program that included medical learners (medical students, family and emergency medicine residents). Focus groups were conducted before and after with nurses, staff physicians, and resident physicians. Phenomenologic analysis using a constructivist framework was used to examine themes. RESULTS: Fifty-two ISSs were held from February 2019 to March 2020. Of those simulations, 36 had learners present. Positive effects included creating an open learning environment and offering staff physicians a safe teaching space. Negative effects arose when objectives of ISS were highly team based or latent safety threat (LST) focused. All groups thought learners added value to ISS. CONCLUSION: Thought should be given to ISS objectives when considering how learners affect other participants. When including learners, review objectives, clarify expectations in prebriefing, and ensure that debriefing begins with LSTs and process objectives before diving into medical objectives. This study provides insight into the effect of medical learners on ISS.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".