Designing and Implementing a Competency-Based Training Program for Anesthesiology Residents at the University of Ottawa
Bibliographic record
Abstract
Competency-based medical education is gaining traction as a solution to address the challenges associated with the current time-based models of physician training. Competency-based medical education is an outcomes-based approach that involves identifying the abilities required of physicians and then designing the curriculum to support the achievement and assessment of these competencies. This paradigm defies the assumption that competence is achieved based on time spent on rotations and instead requires residents to demonstrate competence. The Royal College of Physicians and Surgeons of Canada (RCPSC) has launched Competence by Design (CBD), a competency-based approach for residency training and specialty practice. The first residents to be trained within this model will be those in medical oncology and otolaryngology-head and neck surgery in July, 2016. However, with approval from the RCPSC, the Department of Anesthesiology, University of Ottawa, launched an innovative competency-based residency training program July 1, 2015. The purpose of this paper is to provide an overview of the program and offer a blueprint for other programs planning similar curricular reform. The program is structured according to the RCPSC CBD stages and addresses all CanMEDS roles. While our program retains some aspects of the traditional design, we have made many transformational changes.
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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".