Residents' Perceived Physician-Manager Educational Needs: A National Survey of Psychiatry Residents
Bibliographic record
Abstract
OBJECTIVE: To determine Canadian psychiatry residents' perceived gaps in physician-manager competencies during their residency training. METHODS: Residents at 16 Canadian psychiatry residency programs were mailed an 11-item questionnaire (a copy is available from the authors) assessing their perceived deficiencies in selected managerial knowledge (GSk) and skill (GSs) areas as determined by gap scores (GS). GSs are defined as the difference between residents' perceived current and desired level of knowledge or skill in selected physician-manager domains. Residents' educational preferences were also elicited in the questionnaire. RESULTS: Among the 494 psychiatry residents who were sent the survey, 237 residents (48%) responded. Residents reported the greatest GSk in Program Planning and the greatest GSs in Personal and Professional Self-Care. Predictors of greater total GSks included a lack of previous administrative education during medical school, higher training level, and female sex. Only sex was a significant predictor of total GSss. More than 50% of residents preferred workshops, small groups, mentoring, and didactic learning methods for furthering their knowledge and skills. CONCLUSION: Residents report significant gaps in specific physician-manager training areas, specifically Program Planning, and Personal and Professional Self-Care. The results of this national survey can inform the development of formal physician-manager curricula. To appeal to residents, such curricula should incorporate more interactive pedagogical methods combined with mentoring opportunities.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".