Competency-Based Medical Education: Are Canadian Pediatric Anesthesiologists Ready?
Bibliographic record
Abstract
Background With the introduction of Competency-Based Medical Education (CBME), the Canadian Pediatric Anesthesia Society (CPAS) surveyed its members to assess their awareness of and prior experience with CBME concepts and evaluation tools, and identify methods for faculty development of CBME teaching strategies for pediatric anesthesia residents and fellows. Methods An online survey was sent to CPAS members. Outcomes included respondents' previous exposure to CBME and the educational support they had received in anticipation of the curriculum. Questions used multi-item Likert scales and a general feedback question. Results The response rate was 39% (60/155). Eighty-eight percent of respondents spent ≥50% of their time practicing pediatric anesthesia; 78% and 45% spent at least a quarter of their time teaching residents and fellows respectively. Eighty-three percent were familiar with CBME concepts, and 58% were familiar with Milestones, Competencies, and Entrustable Professional Activities (EPAs). However, 64% had not received any formal training and 52% had not used any CBME evaluation tools. Learning preferences included small group discussions (72%), lectures with questions and answers (Q&A) (62%), seminars (50%), and workshops (50%). Conclusions Despite widespread awareness of CBME concepts, there is a need to educate Canadian pediatric anesthesiologists regarding CBME evaluation tools. Faculty development support will increase the utilization of these tools in teaching practice.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".