Analysis of Supervisors' Feedback to Residents on Communicator, Collaborator, and Professional Roles During Case Discussions
Bibliographic record
Abstract
BACKGROUND: Literature examining the feedback supervisors give to residents during case discussions in the realms of communication, collaboration, and professional roles (intrinsic roles) focuses on analyses of written feedback and self-reporting. OBJECTIVES: We quantified how much of the supervisors' verbal feedback time targeted residents' intrinsic roles and how well feedback time was aligned with the role targeted by each case. We analyzed the educational goals of this feedback. We assessed whether feedback content differed depending on whether the residents implied or explicitly expressed a need for particular feedback. METHODS: This was a mixed-methods study conducted from 2017 to 2019. We created scripted cases for radiology and internal medicine residents to present to supervisors, then analyzed the feedback given both qualitatively and quantitatively. The cases were designed to highlight the CanMEDS intrinsic roles of communicator, collaborator, and professional. RESULTS: Radiologists (n = 15) spent 22% of case discussions providing feedback on intrinsic roles (48% aligned): 28% when the case targeted the communicator role, 14% for collaborator, and 27% for professional. Internists (n = 15) spent 70% of discussions on intrinsic roles (56% aligned): 66% for communicator, 73% for collaborator, and 72% for professional. Radiologists' goals were to offer advice (66%), reflections (21%), and agreements (7%). Internists offered advice (41%), reflections (40%), and clarifying questions (10%). We saw no consistent effects when residents explicitly requested feedback on an intrinsic role. CONCLUSIONS: Case discussions represent frequent opportunities for substantial feedback on intrinsic roles, largely aligned with the clinical case. Supervisors predominantly offered monologues of advice and agreements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".