ArticleBMC medical education2026
Structural gender inequities in dental specialty training in Türkiye: evidence from a national cross-sectional study.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- An assessment of Turkish dental students' attitudes career intentions and perceptions regarding specialist training.BMC medical education · 2026Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study investigated gender-based disparities in specialty preferences among dental students in Türkiye and evaluated the predictive performance of an artificial intelligence-based gender classification tool (NamSor). This study aimed to determine how these disparities shape specialty distribution and to assess whether AI-based gender identification aligns with verified data.
methodsA cross-sectional descriptive design was employed using publicly available data from Turkish university websites and the Council of Higher Education. Gender was manually verified for all specialty students and compared with NamSor's predictions. Statistical analyses included Chi-square tests, Bonferroni adjustments, Kappa coefficient, accuracy metrics, and ROC curve analysis to determine the agreement between actual and predicted gender classifications.
resultsSignificant gender disparities were observed across specialties (χ²=443.55; p < 0.001). Female students were predominantly represented in Pediatric Dentistry (17.9%) and Restorative Dentistry (12.4%), whereas males were concentrated in Oral and Maxillofacial Surgery (26.5%). Gender distribution also differed according to university type. NamSor demonstrated moderate accuracy (67.9%), with higher sensitivity in females (75.1%) than in males (53.6%). The Kappa coefficient was 0.283, indicating a fair agreement between the actual and predicted gender. Misclassification was notable, as 24.9% of women and 46.4% of men were incorrectly classified. The AUC was 0.64, suggesting a modest discrimination ability.
conclusionsGender-based imbalances persist in dental specialty education in Türkiye, particularly in surgical fields, where women remain underrepresented. The modest performance of the NamSor algorithm underscores that AI-based name classification should be regarded as an exploratory method and applied only with caution, while verified administrative data remain the reference standard for gender identification. These findings highlight the importance of equity-focused policies, mentorship initiatives, and gender-sensitive strategies to support balanced participation and career development in dental education.
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