Access to Specialized Medical Training in Spain and Determinants of Failure in the National Entrance Examination: Econometric Modeling Study
Bibliographic record
Abstract
BACKGROUND: The process of accessing specialized medical training in Spain is a complex issue, involving not only the evaluation of medical knowledge acquired throughout university training but also the interaction of factors of a contextual and structural nature, which can influence the results obtained in the entrance examination. In this context, research on the variables that determine performance in this test is of great relevance form not only an academic but also a social and economic point of view. The interaction among factors such as academic performance, gender, nationality, and timing offers a unique opportunity to evaluate the functioning of the educational system at a critical moment in its recent history. Research that has focused specifically on access to specialized medical training has shown mixed results on how these factors impact examination performance. OBJECTIVE: This study aimed to approximate the factors that determine failure in the entrance test for specialized medical training in Spain with the aim of better understanding the extent to which differences based on sex, nationality, and the context of the COVID-19 pandemic contribute to explaining such failure. METHODS: We carried out econometric modeling of the final results obtained in the entrance examination to specialized medical training and identified the explanatory factors that determine the results, their relevance, effect, and significance. Econometric modeling provides a rigorous framework for estimating the causal effect of different variables on the final examination score. It helps identify not only which variables have an impact on performance but also to what extent they do so and under what conditions. RESULTS: Based on the results obtained in the 2019-2021 test calls (7217 eliminated candidates), academic records (P<.001) and examination scores (P<.001), together with demographic factors including sex (P=.54) and nationality (P<.001), and calendar year (P<.001) were determinants of the behavior observed in the final results. Our results do not indicate whether being male or female favors or decreases the final grade obtained; however, being Spanish constitutes a relevant explanatory factor in our final results. The calendar effect, directly related to the COVID-19 pandemic, allows us to quantify the negative impact on the final results. CONCLUSIONS: This study investigated the impact of factors such as sex, nationality, and the COVID-19 pandemic on access to specialized medical training in Spain. Empirically, not being Spanish acts as an unfavorable fixed characteristic in the baseline econometric model, but it becomes favorable when considering the candidate's academic record. The impact of language is not perceived as a limiting factor; the test exclusively evaluates knowledge of medical content. The negative effects of the COVID-19 pandemic are visualized in the final scores.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".