Teaching and learning the physician manager role: Psychiatry residents’ perspectives
Bibliographic record
Abstract
BACKGROUND: Despite widespread consensus that additional training in administration is needed to prepare physicians for practice, little is known about how best to teach managerial competencies and how to integrate teaching into existing postgraduate curricula. AIM: This study aimed to elicit resident perspectives on administrative curriculum development following exposure to a pilot physician manager curriculum at the University of Toronto. METHODS: The authors held five focus groups of psychiatry residents at the University of Toronto during 2008, engaging 40 trainees. Resident perspectives on barriers to teaching and learning administrative skills, preferred curriculum content and format and suggestions for integration of administrative training into the residency programme were elicited. RESULTS: Identified barriers to learning include lack of physician manager role clarity, dearth of learning opportunities and multiple competing demands on residents' time. Residents value a formal administrative curriculum and propose additional opportunities for experiential learning such as elective rotations and mentorship opportunities. Suggested strategies for integrating administrative teaching into residency include faculty development, rotation-specific administrative objectives and end of rotation resident evaluations. CONCLUSION: Our findings provide valuable learner input into an emerging educational framework aiming to address barriers to teaching administrative skills during residency and facilitate longitudinal reinforcement of learning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".