Medical Education Research and the Hierarchy in Medical Training: An Interview with Dr. Dylan Bould
Bibliographic record
Abstract
AbstractDr. Dylan Bould is an anesthesiologist at CHEO (Children’s Hospital of Eastern Ontario) and Director of Education Research at the University of Ottawa’s Department of Anesthesiology. Dr. Bould began training in anesthesia in the U.K. and completed fellowships in pediatric anesthesia and medical education at SickKids and St. Michael’s Hospital in Toronto, as well as a pediatric cardiac anesthesia fellowship in London, England. Over the course of his fellowships in Toronto, Dr. Bould completed a Master of Education at the University of Toronto focusing on medical education. Dr. Bould is also involved in global health, having worked in Nepal and Kenya, and was part of the organization process of the University of Zambia Anesthesia Residency Program. Dr. Bould’s current research focuses on hierarchy in medical training, mentorship in medical education, and simulation in healthcare education. RésuméDr Dylan Bould est un anesthésiologiste au Centre hospitalier pour enfants de l’est de l’Ontario (CHEO) et le directeur de la recherche en enseignement au département d’anesthésie de l’Université d’Ottawa. Dr Bould a commencé sa formation en anesthésie au Royaume-Uni et a complété des formations complémentaires (fellowships) en anesthésie pédiatrique et en enseignement médical à SickKids et à l’Hôpital St Michael à Toronto, ainsi qu’une formation en anesthésie cardiaque pédiatrique à Londres, en Angleterre. Au cours de ses formations complémentaires à Toronto, Dr Bould a complété une maîtrise en éducation à l’Université de Toronto axée sur l’enseignement médical. Dr Bould est également impliqué dans le domaine de la santé mondiale, ayant travaillé au Népal et au Kenya, et ayant aidé à mettre sur pied le programme de résidence en anesthésie à l’Université de la Zambie. La recherche actuelle de Dr Bould se concentre sur la hiérarchie présente lors de la formation médicale, le mentorat en enseignement médical, et la simulation dans l’enseignement des soins de santé.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.022 |
| 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".