Beyond competence: rethinking continuing professional development in the age of competence-based medical education
Bibliographic record
Abstract
[para. 2]: "We have spent many years focusing intently on the first 6–9 years of emergency medicine training. While this is an exciting milestone, the CBME era brings in new challenges for the learning we must do beyond residency. Now that our training programs have been aligned with CBME, it is now time to turn our attention to education beyond training—or, continuing professional development (CPD). At the individual level, the Future of Medical Education in Canada CPD (FMEC CPD) report suggests that physicians should (1) be engaged in CanMEDS/CanMEDS-FM aligned competency-based CPD; (2) be provided with tools and strategies to document and at times revise their scope of practice; (3) focus on competencies related to team functioning and collaboration; (4) be skilled lifelong learners; and (5) engage in continuous practice improvement based on their individual or aggregate practice data. And yet, will this be sufficient? Do we know what it means to be competent in practice? To maintain that competence over time?"
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 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 | 0.088 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.033 | 0.046 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".