Attending Physician Variability
Bibliographic record
Abstract
PURPOSE: There is wide variability in how attending physician roles on teaching teams, including patient care and trainee learning, are enacted. This study sought to better understand variability by considering how different attendings configured and rationalized direct patient care, trainee oversight, and teaching activities. METHOD: Constructivist grounded theory guided iterative data collection and analyses. Data were interviews with 24 attending physicians from two academic centers in Ontario, Canada, in 2012. During interviews, participants heard a hypothetical presentation and reflected on it as though it were presented to their team during a typical admission case review. RESULTS: Four supervisory styles were identified: direct care, empowerment, mixed practice, and minimalist. Driven by concerns for patient safety, direct care involves delegating minimal patient care responsibility to trainees. Focused on supporting trainees' progressive independence, empowerment uses teaching and oversight strategies to ensure quality of care. In mixed practice, patient care is privileged over teaching and is adjusted on the basis of trainee competence and contextual features such as patient volume. Minimalist style involves a high degree of trust in senior residents, delegating most patient care, and teaching to them. Attendings rarely discussed their styles with the team. CONCLUSIONS: The model adds to the literature on variability in supervisory practice, showing that the four styles reflect different ways of responding to tensions in the role and context. This model could be refined through observational research exploring the impact of context on style development and enactment. Making supervisory styles explicit could support improvement of team competence.
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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 | 0.010 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".