AMEE 2010 symposium: Medical student education in the twenty-first century – A new Flexnerian era?
Bibliographic record
Abstract
As we mark the 100th anniversary of the Flexner report which revolutionized the process of medical education, there is again concern that we face a critical need for change in the process of medical education in order to meet the needs of learners, teachers, and patients. In this symposium, panelists shared perspectives on medical education reform from throughout the world, including The Future of Medical Education in Canada, the role of regulators in contributing to reform, the evolution of accreditation standards, the current state of medical education in Southeast Asia, and the perspectives of a medical student on medical education reform. In the "Audience discussion" section, themes emerged surrounding medical education as a social good, the need for governmental support of medical education, the cost of medical education and the rise of for-profit medical schools, and embracing a broader view of health professional education. There remain remarkable parallels in calls for reform in medical education at the turn of the twentieth and twenty-first centuries but education which is patient-centered and actively involves the voices of our patients and our students is likely to be a hallmark.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.021 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".