Learning From Clinical Supervisor Practice Variability: Exploring Medical Resident and Fellow Experiences and Interpretations
Bibliographic record
Abstract
PURPOSE: Clinical practice variability is characterized by 2 or more clinicians making different treatment decisions despite encountering a similar case. This study explores how medical residents and fellows experience and interpret intersupervisor clinical practice variability and how these variations influence learning. METHOD: Seventeen senior residents or fellows in internal medicine, hematology, or thrombosis medicine (postgraduate year 3 or above) participated in semistructured interviews after a clinical rotation in thrombosis medicine from December 2019 to March 2021. Data collection and analysis occurred iteratively and concurrently in a manner consistent with constructivist grounded theory. Variation theory was used to guide the development of some interview questions. A central tenet of this theory is that learning occurs by experiencing 3 sequential patterns of variation: contrast, generalization, and fusion. Participants were recruited purposively with respect to specialty until theoretical sufficiency was reached. RESULTS: Clinical practice variability was experienced by all participants. Residents and fellows attributed practice variability to intrinsic differences among supervisors; interinstitutional differences; selection and interpretation of evidence; patient preferences, priorities, and fears; and their own participation in the decision-making process. Clinical practice variability helped residents and fellows discern key features of cases that influenced decision-making (contrast), group similar cases so that the appropriate evidence could be applied (generalization), and develop attitudes consistent with providing individualized patient care (fusion). Observing practice variability was more helpful for fifth- and sixth-year residents and less helpful for third- and fourth-year residents. CONCLUSIONS: Clinical practice variability helped residents and fellows discern critical aspects, group similar patients, and practice individualized medicine. Future research should characterize how clinical practice variability influences learning across the spectrum of training, how supervisors could encourage learning from practice variability, and how curricula could be modified to allow learners greater opportunity to reflect on and consolidate the practice differences they observe.
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".