Bibliographic record
Abstract
A four-year curriculum in preventive medicine would require planning, but all the components are already available. This article outlines a four-part plan: develop the desired objectives or competencies; present the basics in years one and two of the curriculum; in years three and four make health promotion/disease prevention (HPDP) and the population perspective relevant to the practice of medicine; and, finally, develop a mechanism to track the curriculum and then improve it. Core competencies have already been developed, through joint activities of the Association of Teachers of Preventive Medicine (ATPM) and the Bureau of the Health Professions of the Health Resources and Services Administration (HRSA), and articles about teaching preventive medicine in multiple disciplines throughout the curriculum are published elsewhere in this supplement. Schools across the United States and Canada have innovative programs in place that can serve as models, and there are feasible approaches to monitoring the programs.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.022 |
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".