Assessing gender trends in Canadian urology
Notice bibliographique
Résumé
INTRODUCTION: The number of female medical students and physicians entering the workforce is increasing. Despite this trend, some surgical specialties are still considered male-dominant. Urology has a significant male predominance in both residency and independent practice. This male predominance could have an impact on the physician work force, mentorship opportunities for females pursuing surgery, and on medical student attraction to urology as a specialty. Research conducted in the U.S. has shown that although fewer females enter the field of urology, acceptance rates between the two genders are similar. This study aims to identify if a trend towards gender-specific acceptance into urology residency exists within Canada. We also seek to identify if gender trends in acceptance to urology differ from other surgical specialties in Canada and assess the current workforce trends in Canadian urological practice. METHODS: Logistic regression analyses were used to assess if any significant difference exists between the rates of female and male applicant acceptance into urology. These rates were then compared to the rates of female and male acceptance into surgical residency as a whole and to specific surgical specialties, such as general surgery, orthopedics, and otolaryngology. RESULTS: Within urology applicants, there is no evidence that the success rate over time between males and females differs (p=0.47). Within surgical residency applicants, there is no evidence that the success rate over time differs between male and female applicants (p=0.84). In comparing these two rates, there is also no significant difference between rates of acceptance to urology vs. surgery in general for female applicants (p=0.45). General surgery has a higher growth of females entering into the specialty compared to urology (p=0.026). Conversely, otolaryngology (p=0.123) and orthopedics (p=0.163) did not show a significant difference in the rates of female acceptance as compared to males over time. Our small sample size of 451 applicants over the 10-year time span (122 female, 329 male) could represent a limitation, however, we did ensure to analyze a 10-year sample to attempt to get an accurate representation of any trends. CONCLUSIONS: Our data identifies that there is no significant trend toward male acceptance into urology over female applicants. There is no significant difference related to female acceptance specifically into urology or any difference between rates of females accepted into urology as compared to all other surgical subspecialties combined.
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 | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,007 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».