Comparison between Mexican and International Medical Graduates’ scores in the ENARM Competing for Clinical Specialities in Mexico during 2012-2019: Data Visualization, Trends and Forecasting Analyses
Bibliographic record
Abstract
Objectives: Because there is heterogeneity in the ENARM scores obtained between Mexicans and International medical graduates (IMG) in the eight clinical specialities with direct-entry (Anesthesiology, and Emergency Medicine. Geriatrics, Internal Medicine, Medical Genetics, Pediatrics, Pneumology, Psychiatry), we aimed to evaluate those scores. We hypothesized that Mexican test-takers achieve higher scores than IMG with significant growth trends in their exam scores. Methods: This study was cross-sectional, used historical data from the annual public report of the ENARM for eight years (2012 to 2019). We compare the minimum (MinSco) and maximum (MaxSco) scores of each speciality using ANOVA. Mexican versus IMG scores were evaluated with an independent student t-test, trends with Spearman’s correlation coefficient, and a 5-years forecasting trend. Results: There was a significant difference among the MinSco for five surgical specialities; F (7, 115) = 26.611, p = < .001; the global mean of MinSco was 69.133; specialities above this mean were Internal Medicine, Anesthesiology, Pediatrics, and Pneumology. The global mean for MaxSco was 79.422; five specialities were above: Internal Medicine, Pneumology, Geriatrics, Psychiatry, and Medical Genetics. We did not find a significant difference in the MinSco between Mexicans and IMG, but a significant difference was found in the MaxSco between both groups. Conclusions: ENARM represents a market of high-performance test-takers across the clinical specialities. Mexicans and IMG achieved similar entrance scores, but Mexicans showed a higher MaxSco over IMG in all clinical specialities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".