Bibliographic record
Abstract
Many forces, including the influential report of Abraham Flexner, acted to reform medical education in the early 20th century. Most physicians were not prepared to adopt recent advances in health care due to their poor medical training. This deficit was recognized in the 20 years before Flexner's report by several organizations, including the Illinois State Board of Health, the American Medical Association, and the Association of American Medical Colleges. Before 1910, each organization had engaged in at least one review of medical schools using defined standards and had identified many of the existing deficits. The number of medical schools already had begun to decrease, dropping from 160 in 1905 to 133 in 1910. Flexner drew heavily, but not exclusively, on the standards for medical education previously developed by other organizations. He visited 155 medical schools in the United States and Canada between December 1908 and April 1910. His 1910 report included a conceptual model of how modern medical education should be conducted and descriptions of each medical school that were explicit in both praise and censure.In the decade following the Flexner Report the number of medical schools decreased from 133 to 85. The actions of state medical licensing boards to deny recognition to poor schools sealed their fate. The remaining schools had higher entrance requirements, longer terms, and better resources. The author describes key factors that contributed to the success of the changes recommended by Flexner and others, and then posits why Flexner is still remembered.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".