Factors Affecting Medical Students’ Interest in Obstetrics & Gynecology
Notice bibliographique
Résumé
Background: From 2015 to 2024 the number of residency applications to OB-GYN have gone up 17.4%, from 1,829 to 2,1481.Despite the rise in residency applications to OB-GYN in the United States, no recent studies have examined factors that influence or deter osteopathic medical students from pursuing this field. A small number of similar studies have been conducted in other countries, with recurring deterrents to the field being lifestyle, gender disproportions (for male students), and litigation concerns. The aim of this study is to investigate factors influencing American osteopathic medical students’ interest in OB-GYN, with a special focus in the differences between male and female responses. Methods: Thhis Survey was offered to 1,200 osteopathic medical students in CREOG District V during 2021-2022 academic year. The factors examined included but are not limited to: demographics, level of interest/disinterest in the field, hours worked, length of residency, liability, patient population, competitiveness, and female predominance. These factors were then stratified into “non-factor, minor factor, and major factor” in a student’s decision. Respondents were broken down into male and female. Only two responses listed “other” for gender, and as this was not large enough to conduct a distinct analysis, they were excluded from the study. A combination of Pearson’s Chi Squared and Fisher’s Exact Tests used to conduct analyses. Results: ● 392 responses used in data analysis: 242 (62%) female and 150 (38%) male across 4 graduating classes. ● Those interested in OB-GYN more likely to consider prestige, patient continuity, patient population, and ability to perform procedures. Of note, those who specified interest in OB-GYN were all female respondents (n=26). ● Males more likely to cite disinterest, female predominance, and patient population as reasons for deciding against OB-GYN. Females more likely to cite competitiveness, liability, and lifestyle as reasons that would deter them from the field. Discussion: Factors differ between male and female osteopathic medical students regarding a decision of whether or not to pursue a career in OB-GYN. Some factors more easily modifiable than others, specifically, addressing concerns over lifestyle, competitiveness, and liability. This study highlights the importance of accurate clinical education, adequate career counseling, and robust clinical exposures during OB/GYN rotations to provide positive experiences highlighting the breadth of the field.
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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 | 0,001 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».