Accreditation of the Iraqi Medical Colleges: Urgent call for action
Bibliographic record
Abstract
The National Council for Accreditation of Medical Colleges (NCAMC) has been working on accrediting Iraqi medical schools. However, the NCAMC is not internationally recognized by the World Federation for Medical Education (WFME), which is necessary to meet the requirements of the ECFMG's Recognized Accreditation Policy starting in 2024. This policy states that a medical school must have recognized accreditation from an external quality assurance organization. In the future, only medical schools accredited according to this policy will meet the ECFMG's requirements. Medical graduates who want to work, train, register, do research, volunteer, or pursue other opportunities must apply for verification of their primary medical qualification (PMQ). Verification for international medical graduates (IMGs) in the United States, Australia, Canada, Ireland, New Zealand, the United Kingdom, and many other countries is done through the ECFMG's online system called Electronic Portfolio of International Credentials (EPIC).Since Iraqi medical schools do not currently meet international standards, the quality of their education is at risk, and this could affect the future of new graduates. To safeguard Iraqi medical education and the future of new doctors beyond 2024, the Ministry of Higher Education, universities, medical colleges, and the Iraqi Medical Association (IMA) should collaborate with the NCAMC to gain recognition from the WFME.
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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 | 0.120 | 0.158 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.013 | 0.019 |
| Research integrity | 0.069 | 0.050 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".