Medical Colleges in Saudi Arabia: Can We Predict Graduate Numbers?
Bibliographic record
Abstract
The shortage of Physicians is a major problem in many countries. Medical colleges are often encouraged to increase the graduate numbers. In the Kingdom of Saudi Arabia, Saudi physicians form only 37.89% of the physician manpower. The remainder of the physicians are expatriates. It was recently estimated that the Kingdom would need 29,128 physicians by the year 2014 in order to maintain the same physicians-to-population ratio, i.e., 2.7 per 100,000. To solve the problem, the higher education institutions invested in medical education and increased their number of college graduates. However, there are many questions about whether this strategy will proof successful in covering the shortage or whether this will lead to problems, for example an excess in the number of physicians. These can only be answered if future graduate numbers are estimated and kept under control. In this study, common data mining techniques were reviewed and applied to the output of Saudi medical colleges over the past years. These techniques can be used to predict graduate numbers. The results show the importance of developing a national student information center to fix the data collection problems described in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".