Observational Study of Conformity in Yet Another Medical Learning Environment: Conformity to Preceptors During High-Fidelity Simulation
Bibliographic record
Abstract
Tanya N Beran,1 Ghazwan Altabbaa,2 Elizabeth Oddone Paolucci3 1Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; 2Department of Medicine, University of Calgary and Rockyview General Hospital, Calgary, Alberta, Canada; 3Department of Community Health Sciences and Department of Surgery, University of Calgary, Calgary, Alberta, CanadaCorrespondence: Tanya N Beran, Department of Community Health Sciences, University of Calgary, 3330 Hospital Dr. N.W, Calgary, Alberta, T2N 4N1, Canada, Tel +1 403 220 5667, Fax +1 403 210 7507, Email tnaberan@ucalgary.caPurpose: Altering one’s behavior to comply with inaccurate suggestions made by others (i.e., conformity) has been studied since the 1950s. Although several studies have documented its occurrence in medical education, it has yet to be examined in a high-fidelity simulation environment. It was hypothesized that a large majority of learners would conform to a preceptor.Patients and Methods: A total of 42 student dyads (a medical student paired with a resident) participated in one of four clinical scenarios to manage the diagnosis and treatment of a simulated patient encounter. Once the learners became familiar with the patient’s case, a preceptor entered the simulation, offered an equivocal suggestion about diagnosis or management, and then left. Two raters observed the video recordings of how the learners managed the case after this suggestion was made. The nature of these interactions was also documented.Results: Sixteen (38.10%) of the 42 medical student dyads conformed to the equivocal information presented by the preceptors. Observations of these interactions showed that all of the medical students conformed to the residents, but not all of the medical students conformed to the preceptors.Conclusion: Many learners conform to preceptors by acting on their equivocal suggestion when managing a patient case during high-fidelity simulation.Keywords: Adherence, medical students, Immersive learning, medical education, medical errors, patient safety
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".