The advancement of medical education through innovative research and simulation learning: a discussion with Dr. Viren Naik, Medical Director of the University of Ottawa Skills and Simulation Centre
Bibliographic record
Abstract
A B S T R A C T\nThe spotlight of UOJM’s 4th issue is medical education. We met with Dr. Viren Naik, anesthesiologist, associate professor at the University\nof Ottawa (uOttawa), and Medical Director of the University of Ottawa Skills and Simulation Centre (uOSSC). He is also a core\nteam member of the Academy of Innovation in Medical Education (AIME), uOttawa’s centre for advancing medical education research.\nDr. Naik is actively involved in research, with over 60 peer-reviewed publications and grants. He was also the previous chair of the\nWritten Examination in Anesthesia with the Royal College of Physicians and Surgeons of Canada. In this interview, we discuss the advancement\nof medical education with the skills and simulation centre, the future of the medical curriculum, and how to be involved in\nmedical education as students. // R É S U M É\nDans cette 4e édition du JMUO, le sujet mis en lumière est l’éducation médicale. Nous avons rencontré le Dr Viren Naik, anesthésiologiste,\nprofesseur agrégé de l’Université d’Ottawa (uOttawa) et directeur médical du Centre de compétences et simulation de\nl’Université d’Ottawa (CCSUO). C’est aussi un membre important de l’Académie pour l’innovation en éducation médicale (AIME), le\ncentre de l’Université d’Ottawa qui a pour but de faire avancer la recherche en éducation médicale.\nDr Naik est un chercheur très dynamique qui a plus de 60 publications et subventions évaluées par les pairs à son actif. Dans le passé,\nil a aussi présidé l’examen écrit en anesthésiologie du Collège royal des médecins et chirurgiens du Canada. Durant l’entrevue, nous\navons discuté de l’avancement de l’éducation médicale au Centre de compétences et simulation, de l’avenir du cursus médical et de la\nfaçon que les étudiants peuvent participer à l’éducation médicale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".