Improvement of the Medical Education Situation in Sudan: Collegectomy is Not the Only Management Option
Bibliographic record
Abstract
Sudan witnessed an increase in the number of colleges of medicine after the higher education revolution in the early 1990s. Many authors writing about medical education, both in Sudan and across the world, have described a negative correlation between the increased number of medical colleges and the quality of education provided by those colleges. Many educational leaders in Sudan are calling for action to deal with the issues arising from this great expansion of medical colleges, with opinions varying from collegectomies (closure of the colleges) to merging colleges. Several strategies have been implemented in Canada, Iran, the Philippines and South Africa to deal with similar situations. These have included college support such as funding or technical support, changing the colleges’ educational strategies, modifying the curriculum, integrating (rather than merging) colleges, and collegectomies. This paper outlines possible actions to be taken in response to the expansion of medical colleges in the Sudanese context. It explores the international experience with the situation in an attempt to augment the discussion with options that may help to improve medical education. Keywords: collegectomy, medical education, Sudan, social accountability
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".