Patterns of interaction during rounds: implications for work‐based learning
Bibliographic record
Abstract
OBJECTIVES In-patient rounds are a major educational and patient care-related activity in teaching hospitals. This exploratory study was conducted to gain better understanding of team interactions during rounds and to assess student and resident perceptions of the utility of this activity. METHODS Data were collected by a non-participant observer using a novel, personal digital assistant (PDA)-based data collection system. Medical students and residents completed surveys related to the utility of rounds for patient care, education and ward administration. Analyses included descriptive and correlational statistics and the use of social network analysis to describe and measure patterns of interaction. RESULTS Eighteen different rounds were observed. On average, rounds were 106 minutes long and included discussion of 22.1 patients. Three different patterns of verbal interaction were observed. In most cases, the attending physician was most talkative and many students and residents spoke infrequently. More time was devoted to patients discussed earlier in the round, regardless of diagnosis. Observed teaching was primarily factual and teacher-centred. Attending physician-dominated sessions were rated more highly for educational utility than those that were more interactive. CONCLUSIONS In-patient rounds are an example of an opportunity for powerful work-based learning. In this study, we used a novel method of observational data collection and analysis to examine this activity and found that it may not always live up to its educational potential. Rounds are time-consuming and are generally dominated by the attending physician. Individuals who are not directly involved in a case are often minimally involved. Participants felt that rounds were most useful for patient care and, contrary to expectations, students and residents viewed attending physician-dominated sessions as more educational. To improve the educational impact of rounds, the order of patient discussion should be planned to highlight specific teaching points, preceptors (teaching staff) should ensure that all team members are actively engaged in the process and learning should be made explicit.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".