Some Things Change, Some Things Stay the Same: Trends in Canadian Education in Paediatric Cardiology and the Cardiac Sciences
Bibliographic record
Abstract
Education in paediatric cardiology has evolved along with clinical care. The availability and application of new technologies in education, in particular, have had a significant impact. Artificial intelligence; virtual, augmented, and mixed reality learning tools; and gamification of learning have all resulted in new opportunities for today's trainees compared with those of the past. A new training model is also being used. Though currently focused on residency education, competency-based medical education is also being applied to undergraduate education in some Canadian medical schools. Competency-based medical education offers a more transparent relationship between education and physicians' social contract with society. It provides greater accountability for programmes and learners to teach and learn the skills required to function as competent specialists. However, it has not come without challenges. Coincident with the application of this model for learners, there has been increased educational accountability for physicians in practice and for the institutions training them. Despite these changes, some things have remained the same. On the positive side, the importance of good clinical teachers to effective learning remains constant. Unfortunately, the mistreatment of learners within our education system also remains and is perhaps the most important challenge facing medical education in Canada today. Learning to be better teachers and learner advocates is an important goal for all of those involved in educating Canadian medical learners.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".