Applicant Factors for Matching Into an Orthopaedic Foot and Ankle Fellowship
Notice bibliographique
Résumé
Background: Over the past 2 decades, various initiatives have aimed to enhance diversity in orthopaedic surgery, promoting greater racial, ethnic, and gender equity. Building on this progress, demographic data on orthopaedic fellowship matches has been collected over the last 3 years. This study seeks to analyze trends in applicants to foot and ankle fellowships, characterize the applicant pool, and identify traits associated with successful matches. Methods: All applicant information from a fellowship application service site was obtained for match years 2022-2024. Applicants were selected only if they applied to the specific subspecialty fellowship. Gender, race, and ethnicity were recorded. Applicant factors such as Alpha Omega Alpha (AOA) status, Gold Humanitarian status, United States Medical Licensing Examination (USMLE) Step 2 score, number of applications, and number of interview invitations were used. Applicant medical school status, including allopathic, osteopathic, Canadian, and foreign medical graduate (FMG) were analyzed. χ 2 test was performed between US and FMG applicants. Univariate and multivariate binomial logistic regression was performed for FMGs. Results: There were 286 applicants, 82.8% males, 16.5% females, 133 US- and Canadian-trained graduates, and 153 FMGs. The match rate for US- and Canadian-trained graduates was 99.2% compared with FMGs, which was 43.7% and associated with lower matching rates ( P < .00001). When performing analysis in US and FMG groups independently because of multicollinearity, no factors could be associated with matching. Only when the applicant had FMG status, then the number of interview invitations were associated with matching. When the number of interviews approached 6, the likelihood of matching was >95%. All except 1 US F&A applicant matched into an F&A fellowship. During the match period, US-trained applicants were 28%-32% female, 4%-8% Black/African American, 8%-17% Asian, 65%-73% White, 2%-4% American Indian, and 2%-8% Hispanic, with no Native Hawaiian and Pacific Islanders applying. Female applicants were above representation compared to Accreditation Council for Graduate Medical Education (ACGME) numbers, but the remaining race and ethnicity applicants were within the range of current ACGME standings, which is still lower than US Census results. Conclusion: Nearly all US-trained foot and ankle applicants matched, whereas FMG applicants matched 43% of the time.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».