Predictive Value of Credit Score on Surgery Resident and Fellow Academic and Professional Performance
Notice bibliographique
Résumé
Introduction Surgical specialties consistently remain among the most competitive residency and fellowship programs with some of the highest rates of unmatched applicants. Attrition in surgical specialties is as high as 30% and particularly problematic given the extended duration of training and limited number of positions. Applicants are traditionally evaluated using a streamlined set of objective metrics, such as board scores, class rank, leadership, letters of recommendation, research productivity, and volunteerism. Consumer credit scores have been shown to be predictors of personality and work performance, however, the literature has yet to explore consumer credit histories in the context of surgical resident and fellow performance. This study aims to determine whether consumer credit scores of surgery residents and fellows are predictive of academic and professional performance. Methods This is a multi-institutional observational survey study across all American Council of Graduate Medical Education and Royal College of Physicians and Surgeons accredited surgical residency and fellowship programs in the United States and Canada. Ninety-nine surgical residents and fellows with educational status of post-graduate year two or higher participated in this study. Dichotomous (yes or no) survey items were formulated to assess performance indicators in the domains of notable achievements and awards, research output, written examination performance, professionalism, and surgical/technical skills. Three-digit Fair Isaac Corporation (FICO) credit scores, a widely accepted consumer reporting score, were collected to avoid calculation variability between algorithms. Results Surgical residents and fellows reported credit scores between 611( fair) and 853 (exceptional) with a median (interquartile range) of 774 (715-833). The majority of participants 51.5%(51) reported very good credit scores. Those with higher credit scores (very good/exceptional) were 377% more likely to have one or more positive performance indicators OR (95% CI) = 3.77 (1.43-9.97). Similarly, residents with lower credit scores (fair/good) were only 40% more likely to have one or more negative performance indicators. The credit score has a moderate ability to distinguish between the presence and absence of positive performance indicators (area under the curve {AUC} = 0.70, p = 0.001). The use of 753 as a credit score cutoff is 78.9% sensitive and 52.4% specific for discerning surgery residents and fellows with one or more positive performance indicators. The credit score did not significantly discern those with negative performance indicators. Conclusions While credit score was significantly functional in discerning those with and without positive performance indicators, sensitivity and specificity rates leave much to be desired. This study suggests credit score may have a utility as a companion to traditional metrics used in identifying candidates for surgery residencies and fellowships who will have positive performance in the domains of research productivity, written examination performance, and professional awards and recognition. Additional studies are needed to assess this utility on a larger scale.
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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 | 0,001 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 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 ».