The advancement of medical education through innovative research and simulation learning: A discussion with Dr. Viren Naik, medical director of the University of Ottawa’s Skills and Simulation Centre
Bibliographic record
Abstract
A B S T R A C TThe spotlight of UOJM’s 4th issue is medical education. We met with Dr. Viren Naik, anesthesiologist, associate professor at the University of Ottawa (uOttawa), and Medical Director of the University of Ottawa Skills and Simulation Centre (uOSSC). He is also a core team member of the Academy of Innovation in Medical Education (AIME), uOttawa’s centre for advancing medical education research. Dr. Naik is actively involved in research, with over 60 peer-reviewed publications and grants. He was also the previous chair of the Written Examination in Anesthesia with the Royal College of Physicians and Surgeons of Canada. In this interview, we discuss the advancement of medical education with the skills and simulation centre, the future of the medical curriculum, and how to be involved in medical education as students. R É S U M ÉDans cette 4e édition du JMUO, le sujet mis en lumière est l’éducation médicale. Nous avons rencontré le Dr Viren Naik, anesthésiologiste, professeur agrégé de l’Université d’Ottawa (uOttawa) et directeur médical du Centre de compétences et simulation de l’Université d’Ottawa (CCSUO). C’est aussi un membre important de l’Académie pour l’innovation en éducation médicale (AIME), le centre de l’Université d’Ottawa qui a pour but de faire avancer la recherche en éducation médicale. Dr Naik est un chercheur très dynamique qui a plus de 60 publications et subventions évaluées par les pairs à son actif. Dans le passé, il a aussi présidé l’examen écrit en anesthésiologie du Collège royal des médecins et chirurgiens du Canada. Durant l’entrevue, nous avons discuté de l’avancement de l’éducation médicale au Centre de compétences et simulation, de l’avenir du cursus médical et de la façon que les étudiants peuvent participer à l’éducation médicale.
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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 | 0.041 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.018 | 0.046 |
| 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".