A landscape analysis of leadership training in postgraduate medical education training programs at the University of Ottawa
Bibliographic record
Abstract
BACKGROUND: There is growing recognition of the importance of physician leadership in healthcare. At the same time, becoming an effective leader requires significant training. While educational opportunities for practicing physicians exist to develop their leadership skills, there is a paucity of leadership opportunities for post graduate trainees. In response to this gap, both the Royal College of Physicians and Surgeons of Canada and the Association of Faculties of Medicine of Canada have recommended that leadership training be considered a focus in Post Graduate Medical Education (PGME). However, post-graduate leadership curricula and opportunities in PGME training programs in Canada are not well described. The goal of this study was to determine the motivation for PGME leadership training, the opportunities available, and educational barriers experienced by PGME programs at the University of Ottawa. METHODS: An electronic survey was distributed to all 70 PGME Program Directors (PDs) at the University of Ottawa. Two PDs were selected, based on strong leadership programs, for individual interviews. RESULTS: The survey response rate was 55.7%. Seventy-seven percent of responding PDs reported resident participation in leadership training as being "important," while only 37.8% of programs incorporated assessment of resident leadership knowledge and/or skills into their PGME program. Similarly, only 29.7% of responding residency programs offered chief resident leadership training. CONCLUSIONS: While there is strong recognition of the importance of training future physician leaders, the nature and design of PGME leadership training is highly variable. These data can be used to potentially inform future PGME leadership training curricula.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".