The Pass/Fail Effect: A Longitudinal Study of United States Medical Licensing Examination (USMLE) Step 1 Performance Over a Decade
Notice bibliographique
Résumé
Objectives This study aimed to analyze the impact of the United States Medical Licensing Examination (USMLE) Step 1 transition to a pass/fail scoring system in 2022 on the performance of first-time test takers in three distinct groups: Doctor of Osteopathy (DO) and Doctor of Medicine (MD) examinees from US/Canadian schools and examinees from non-US/Canadian schools. The analysis spans a decade-long period from 2012 to 2022, offering insights into the implications of this pivotal change in medical education. Methods We analyzed the performance of first-time USMLE Step 1 examinees from US/Canadian MD and DO programs and non-US/Canadian schools from 2012 to 2022, including the transition year to a pass/fail scoring system. Data were obtained from USMLE performance data reports and organized into annual contingency tables. Descriptive statistics and comparative analysis were used to identify trends and differences in performance across the groups. Data visualization techniques were employed to illustrate these findings, and the results were contextualized within the broader changes in medical education. Results In 2021, first-time takers from US/Canadian MD and DO Degree programs had pass rates of 96% and 94%, respectively, while non-US/Canadian schools had a pass rate of 82%. However, in 2022, these rates dropped to 93%, 89%, and 74%, respectively. The most significant relative decline was observed among non-US/Canadian Schools' first-time takers, with an 8% decrease. Repeaters consistently had lower pass rates across all groups. Conclusion The study reveals a notable decline in pass rates following the transition to pass/fail scoring, although this is based on just one year of data. This underscores the importance of students not rushing into the exam and dedicating sufficient time for preparation. The potential impact of this research could be transformative for medical education, but more years of data post-transition will be needed to confirm these initial findings. These findings serve as a reminder that the change in scoring does not diminish the rigor of the exam, prompting students to approach their studies with diligence and patience and potentially paving the way for systemic improvements in medical education and healthcare delivery worldwide.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».