Teaching and learning in morbidity and mortality rounds: an ethnographic study
Bibliographic record
Abstract
OBJECTIVES In keeping with the current emphasis on quality improvement and patient safety, a Canadian division of general internal medicine began holding weekly morbidity and mortality rounds (M&MRs) with postgraduate trainees. Grounded in the medical education and social sciences literatures about such rounds, we sought to explore the teaching and learning processes that occur in M&MRs in order to understand their role in, and contribution to, the current medical education context. METHODS We conducted an ethnography of these M&MRs. We observed the rounds, conducted interviews with both staff doctors and residents and triangulated the resultant data. Concurrent, iterative data collection and analysis enabled sampling to saturation. RESULTS Staff doctors had differing understandings of the role of M&MRs and valued different kinds of teaching. They did not think they were teaching medical content knowledge at these rounds, but rather that they were role-modelling six skills, attitudes and behaviours, including 'identifying and addressing process and systems issues affecting care'. Residents primarily wanted to learn content knowledge and tried to extract such knowledge out of the rounds. They did recognise and value that they were learning about process and systems issues. They also agreed that staff doctors were role-modelling other things, but had varying perceptions of what those were; most did not value this role-modelled learning as much as they valued the acquisition of content knowledge. CONCLUSIONS These M&MRs were effective forums for addressing patient safety and quality improvement competencies. They carried none of the negative functions attributed to such rounds in the sociology literature, focusing neither on absolving responsibility nor on learning socially acceptable ways to discuss death in public. However, this study revealed a marked disjunction between the teaching valued by staff doctors and the learning valued by their trainees.
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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 | 0.003 | 0.095 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".