Factors Influencing Preferences in International Ophthalmology Subspecialty Fellowships Among Young Ophthalmologists in Türkiye: A Descriptive Study
Bibliographic record
Abstract
Objective: This study explores the interest in international ophthalmology subspecialty fellowship training among young ophthalmologists in Türkiye, focusing on demographic factors, motivations, and barriers. Material and Methods: A cross-sectional study was conducted using an anonymous online survey from April to July 2024. A total of 232 ophthalmologists from 14 institutions were included. Participants were categorized into 3 groups: no interest (Group 1), interested but not applied (Group 2), and active applicants or past participants (Group 3). Factors associated with interest and application were assessed using Pearson's chi-square test in bivariate analyses and multinomial logistic regression analysis in multivariate analyses. Results: The mean age was 28.9±3.6 years; 48.3% were female. Group 1 included 43 participants (18.5%), Group 2 had 126 (54.6%), and Group 3 had 63 (27.2%). The most preferred subspecialties were cataract and refractive surgery (72%), cornea and ocular surface diseases (52.6%), and oculoplastic surgery (52.6%), with the latter significantly higher among residents. Most preferred destinations were the United Kingdom, the United States, Germany, and Canada. Career development was the primary motivator, while familial obligations were the main barrier. Specialists, those knowledgeable about opportunities, and those proficient in foreign languages were more likely to apply (p<0.05). Multivariate analysis revealed foreign language proficiency as a key differentiating factor between interest and non-interest groups (p<0.001). Conclusion: There is a strong interest in international ophthalmology subspecialty fellowships among young ophthalmologists in Türkiye. This study highlights significant demographic, sociological, and academic factors influencing this interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".