Reforming Medical Education in Ethics and Humanities by Finding Common Ground With Abraham Flexner
Bibliographic record
Abstract
Abraham Flexner was commissioned by the Carnegie Foundation for the Advancement of Teaching to conduct the 1910 survey of all U.S. and Canadian medical schools because medical education was perceived to lack rigor and strong learning environments. Existing proprietary schools were shown to have inadequate student scholarship and substandard faculty and teaching venues. Flexner's efforts and those of the American Medical Association resulted in scores of inadequate medical schools being closed and the curricula of the survivors being radically changed. Flexner presumed that medical students would already be schooled in the humanities in college. He viewed the humanities as essential to physician development but did not explicitly incorporate this position into his 1910 report, although he emphasized this point in later writings. Medical ethics and humanities education since 1970 has sought integration with the sciences in medical school. Most programs, however, are not well integrated with the scientific/clinical curriculum, comprehensive across four years of training, or cohesive with nationally formulated goals and objectives. The authors propose a reformation of medical humanities teaching in medical schools inspired by Flexner's writings on premedical education in the context of contemporary educational requirements. College and university education in the humanities is committed to a broad education, consistent with long-standing tenets of liberal arts education. As a consequence, premedical students do not study clinically oriented science or humanities. The medical school curriculum already provides teaching of clinically relevant sciences. The proposed four-year curriculum should likewise provide clinically relevant humanities teaching to train medical students and residents comprehensively in humane, professional patient care.
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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 | 0.035 | 0.028 |
| 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 | 0.058 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".