Communication in the conversation between preceptors and physicians-in-training during simulation: what is not said
Bibliographic record
Abstract
BACKGROUND: the sharing of one's inner thoughts and feelings. This conceptualization of communication was applied to guide our understanding of how medical learners interact with preceptors at the bedside in a high-fidelity simulation when managing a patient case. METHODS: A total of 84 medical learners (42 residents and 42 medical students) participated in a high-fidelity simulation. After they interacted with the patient for about 10 min, a preceptor entered and offered an equivocal or questionable recommendation about diagnosis or treatment. This type of recommendation was designed to trigger a difficult conversation that would create an opportunity for the learners to share facts, thoughts, points of view, and feelings about the patient with the preceptor. The preceptor left the room, and the learners completed their assessment once they made a diagnosis and treatment recommendations. Two raters independently coded the communication between the preceptor and learners by independently watching video recordings. RESULTS: = 56, 66.70%) engaged in a muted conversation where they shared little or no clarification of facts about the patient's case, their feelings or thoughts, nor did they explore their preceptor's point of view. CONCLUSIONS: Learners may not feel comfortable exploring or expressing thoughts and feelings in front of their preceptors. We recommend that preceptors directly engage learners in conversation.
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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 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".