Bibliographic record
Abstract
We thank Dr. Stoller for bringing the Cleveland Clinic Chief Residents Leadership Workshop to our attention. Neither article on this topic was retrieved through our search strategy.1,2 However, the authors’ internal medicine leadership training program at the Cleveland Clinic, which appears to be an earlier iteration of the chief residents program,3 was included in our meta-analysis. We applaud Dr. Stoller and his colleagues for early recognition of the need for leadership development in postgraduate medical education and for continuing to develop and refine their program. We agree that emotional intelligence (EI) is an important element in leadership training, but believe that character-based leadership (CBL) is a more comprehensive lens for developing physician leaders. There is increasing recognition that EI skills can be used for emotional manipulation if developed disingenuously.4,5 The Ivey CBL framework emphasizes the 3 C’s—character, competency, and commitment—and describes character as a “habit of being” that can withstand situational pressures.6 We are currently developing a leadership course for postgraduate medical trainees by drawing on the Ivey CBL framework. See relevant CBL resources here: https://www.dropbox.com/sh/eb27zex4053mg0v/AAAe9HzhoNLH-JjrMUwLgXKna?dl=0. Wael Haddara, MDAssociate professor, Divisions of Critical Care Medicine & Endocrinology and Metabolism, Department of Medicine, and Centre for Education Research and Innovation, Schulich School of Medicine & Dentistry, Western University, London, Ontario; ORCID: https://orcid.org/0000-0002-9817-5524.Jacqueline Torti, PhDResearch consultant & education specialist, Centre for Education Research and Innovation Schulich School of Medicine & Dentistry, Western University, London, Ontario; ORCID: https://orcid.org/0000-0003-4518-0255.Ali InayatResearch assistant, Centre for Education Research and Innovation, Schulich School of Medicine & Dentistry, Western University, London, Ontario; [email protected]Nabil Sultan, MDAssociate professor, Division of Nephrology, Department of Medicine, and Centre for Education Research and Innovation researcher, Schulich School of Medicine & Dentistry, Western University, London, Ontario; ORCID: https://orcid.org/0000-0002-3130-1856.
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 | 0.008 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.045 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".