“Patients Are the People Who Teach Me the Most”: Exploring the Development of Communication Skills During Internal Medicine Residency
Bibliographic record
Abstract
Background: Physician-patient communication training is a vital component of medical education, yet physicians do not always achieve the communication expertise expected of them. Despite extensive literature on the efficacy of various training interventions, little is known about how residents believe they learn to communicate. Objective: To understand residents' perspectives on the development of their communication skills. Methods: Between November 2020 and January 2021 recruitment emails were sent to all 225 internal medicine residents at the University of Toronto; one-on-one interviews were conducted with 15 residents. Participants were asked to reflect on communication skills development. Interviews were conducted and analyzed using constructivist grounded theory. Results: Participants credited the majority of their skills development to unsupervised interactions with patients, without explicit guidance from an attending physician. Attendings' contributions were primarily seen through role modeling, with little perceived learning coming from feedback on observed interactions. This was partly explained by residents' proclivity to alter their communication styles when observed, rendering feedback less relevant to their authentic practice, and by receiving generically positive feedback lacking in constructive features. Time constraints led to communication styles that prioritized efficiency at the cost of patient-centeredness. Conclusions: These findings suggest that current models of communication training and assessment may fall short due to overreliance on observation by attendings and examiners, which may fail to unearth the authentic and largely self-taught communication behaviors of residents. Further research is required to ascertain the feasibility and potential value of other forms of communication training and assessment, such as through patient feedback.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".