A retrospective and prospective look at medical education in the United States: trends shaping anatomical sciences education
Bibliographic record
Abstract
During the last decade of the 20th century and the first decade of the 21st century, curricular reform has been a popular theme. In fact, reform on the current scale has not occurred since the early 1900s, when Abraham Flexner released his landmark report 'Medical Education in the United States and Canada'. His report, suggesting major changes in how physicians were educated, became the norm and few changes occurred until the last quarter of the 20th century. During this period increased demands on medical school curriculums due to the explosion of knowledge in biomedical sciences and the pressure to add additional clinical experiences increased the momentum for curriculum reform. In 1984 an Association of American Medical Colleges (AAMC) report, 'Physicians for the Twenty-First Century: The Report of the Panel on the General Professional Education of the Physician (GPEP) and College Preparation for Medicine', discussed many items related to reforming medical education including the value of integration, increased use of active learning formats, more self-directed learning, improved communication skills and increased problem-solving activities. This was followed by a report released in 1993 entitled 'Educating Medical Students: Assessing Change in Medical Education - The Road to Implementation' (ACME-TRI), which identified educational problems by surveying medical school deans, suggested ways to deal with these issues and presented a plan of action. Recently, the Carnegie Foundation for the Advancement of Teaching released 'Education Physicians: A Call for Reform of Medical School and Residency' with additional suggestions. At this point the question that might be asked is - Where is all this going and how is it going to affect anatomy education?
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".