Medical School Admissions: A Review of Global Practices, Predictive Validity, and Practice Points for Africa
Bibliographic record
Abstract
Background: Processes for selection of candidates into medical schools vary globally. Knowledge of the predictive validity of a selection method is important for policy revision. Aim: To survey the practices used by medical schools to select students and their predictive validity. Methods: Search terms developed from the research problem were used to search Google Scholar, PubMed, and Educational Resources Information Centre (ERIC). These were “medical school,” “predictive validity,” “success,” “academic achievement” “admission criteria,” and “student selection.” Retrieved articles were screened for relevance and sorted according to countries ofpublication. Authors narratively reviewed the articles from each country and collated the findings. Best practices were recommended for African-based medical schools. Results: Articles retrieved from 14 countries were included in the review. USA, Canada, UK, Australia, and New Zealand operate centralized medical school admission programs and administer nationwide admission tests. These tests cover cognitive and non-cognitive domains. The validity of these tests in predicting medical school success were extensively studied and reported. Other countries do not operate centralized medical school admission programs. Most of these rely on cognitive excellence to select students. Few reports are available on the validity of selection practices in Africa. Most rely on cognitive excellence which highly predicted academic success during preclinical studies. Predictivity decreased during clinical phases and non-cognitive variables became better predictors of success. Conclusion: Medical school admission processes should consider cognitive and non-cognitive factors. With non-cognitive factors, candidates with right attitudes are selected. African countries should align their practices to that of Western countries.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".