Access to Specialized Medical Training in Spain and Determinants of Failure in the National Entrance Examination: Econometric Modeling Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».