Paediatric medical education: Challenges and new developments
Bibliographic record
Abstract
Throughout the history of medical education, there have been major changes in how we as paediatricians are trained and, hopefully, in how we provide care to children. In the early 1900s, Flexner (1) reported on his survey of all the medical schools in the United States and Canada, and recommended that medical schools no longer be independent, but associated with universities with sophisticated laboratories and a curriculum solidly based in science and the humanities. High national standards and examinations were developed through the Medical Council of Canada and the Royal College of Physicians and Surgeons of Canada (RCPSC). In the 1970s, Barrows and Tamblyn (2) proposed a curriculum that was student-centred – based on students independently solving and researching specific clinical problems. In the 1980s, it was recognized that teaching cannot be simply experiential and anecdotal, but must be based on evidence in the medical research literature (3). In the 1990s, a project named ‘Educating Future Physicians of Ontario’ and subsequently the Royal College of Physicians and Surgeons of Canada, Canadian Medical Education Directives for Specialists (CanMEDS) project surveyed physicians and the lay public to find out society's expectations of physicians, recognizing that the physician specialist is more than just a medical expert, but also a communicator, collaborator, manager, health advocate, scholar and professional (4,5). In the 21st century, medical schools and medical education programs recognize that we have a responsibility not only to create new knowledge and transfer knowledge to students and medical residents but also a social responsibility. The present paper will report on some of these new developments in undergraduate and postgraduate medical education, and some of the new innovations in medical education, especially linked to the north or rural areas.
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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.021 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".