Medical Education for a Healthier Population: Reflections on the Flexner Report From a Public Health Perspective
Bibliographic record
Abstract
Abraham Flexner's 1910 report is credited with promoting critical reforms in medical education. Because Flexner advocated scientific rigor and standardization in medical education, his report has been perceived to place little emphasis on the importance of public health in clinical education and training. However, a review of the report reveals that Flexner presciently identified at least three public-health-oriented principles that contributed to his arguments for medical education reform: (1) The training, quality, and quantity of physicians should meet the health needs of the public, (2) physicians have societal obligations to prevent disease and promote health, and medical training should include the breadth of knowledge necessary to meet these obligations, and (3) collaborations between the academic medicine and public health communities result in benefits to both parties. In this article, commemorating the Flexner Centenary, the authors review the progress of U.S. and Canadian medical schools in addressing these principles in the context of contemporary societal health needs, provide an update on recent efforts to address what has long been perceived as a deficit in medical education (inadequate grounding of medical students in public health), and provide new recommendations on how to create important linkages between medical education and public health. Contemporary health challenges that require a public health approach in addition to one-on-one clinical skills include containing epidemics of preventable chronic diseases, reforming the health care system to provide equitable high-quality care to populations, and responding to potential disasters in an increasingly interconnected world. The quantitative skills and contextual knowledge that will prepare physicians to address these and other population health problems constitute the basics of public health and should be included throughout the continuum of medical 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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.031 | 0.039 |
| Insufficient payload (model declined to judge) | 0.008 | 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".