Developing a medical school: Expansion of medical student capacity in new locations: AMEE Guide No. 55
Bibliographic record
Abstract
BACKGROUND: A concern about an impending shortage of physicians and a worry about the continued maldistribution of physicians to medically underserved areas have encouraged the expansion of medical school training places in many countries, either by the creation of new medical schools or by the creation of regional campuses. AIMS: In this Guide, the authors, who have helped create new regional campuses and medical schools in Australia, Canada, UK, USA, and Thailand share their experiences, triumphs, and tribulations, both from the views of the regional campus and from the views of the main Medical School campus. While this Guide is written from the perspective of building new regional campuses of existing medical schools, many of the lessons are applicable to new medical schools in any country of the world. Many countries in all regions of the world are facing rapid expansion of medical training facilities and we hope this Guide provides ideas to all who are contemplating or engaged in expanding medical school training places, no matter where they are. DESCRIPTION: This Guide comprises four sections: planning; getting going; pitfalls to avoid; and maturing and sustaining beyond the first years. While the context of expanding medical schools may vary in terms of infrastructure, resources, and access to technology, many themes, such as developing local support, recruiting local and academic faculty, building relationships, and managing change and conflict in rapidly changing environments are universal themes facing every medical academic development no matter where it is geographically situated. FURTHER INFORMATION: The full AMEE Guide, printed separately, in addition contains case examples from the authors' experiences of successes and challenges they have faced.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".