Regional Medical Campuses
Bibliographic record
Abstract
There is burgeoning belief that regional medical campuses (RMCs) are a significant part of the narrative about medical education and the health care workforce in the United States and Canada. Although RMCs are not new, in the recent years of medical education enrollment expansion, they have seen their numbers increase. Class expansion explains the rapid growth of RMCs in the past 10 years, but it does not adequately describe their function. Often, RMCs have missions that differ from their main campus, especially in the areas of rural and community medicine. The absence of an easy-to-use classification system has led to a lack of current research about RMCs as evidenced by the small number of articles in the current literature. The authors describe the process of the Group on Regional Medical Campuses used to develop attributes of a campus separate from the main campus that constitute a "classification" of a campus as an RMC. The system is broken into four models-basic science, clinical, longitudinal, and combined-and is linked to Liaison Committee on Medical Education standards. It is applicable to all schools and can be applied by any medical school dean or medical education researcher. The classification system paves the way for stakeholders to agree on a denominator of RMCs and conduct future research about their impact on medical education.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.013 |
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".