Sex Disparities in Ophthalmology From Training Through Practice
Bibliographic record
Abstract
Importance: Sex-based research in medicine has revealed inequities against females on almost every metric at almost every career stage; ophthalmology is no exception. Objective: To systematically review the experiences of females in ophthalmology (FiO) from training through practice in high-income countries (HICs). Evidence Review: A systematic review of English-language studies, published between January 1990 and May 2022, relating to FiO in HICs was performed. PubMed, MEDLINE, and Embase electronic databases were searched, as well as the Journal of Academic Ophthalmology as it was not indexed in the searched databases. Studies were organized by theme at each career stage, starting in medical school when an interest in ophthalmology is expressed, and extending up to retirement. Findings: A total of 91 studies, 87 cross-sectional and 4 cohort, were included. In medical school, mentorship and recruitment of female students into ophthalmology was influenced by sex bias, with fewer females identifying with ophthalmologist mentors and gender stereotypes perpetuated in reference letters written by both male and female referees. In residency, females had unequal learning opportunities, with lower surgical case volumes than male trainees and fewer females pursued fellowships in lucrative subspecialties. In practice, female ophthalmologists had lower incomes, less academic success, and poorer representation in leadership roles. Female ophthalmologists had a greater scholarly impact factor than their male counterparts, but this was only after approximately 30 years of publication experience. Pervasive throughout all stages of training and practice was the experience of greater sexual harassment among females from both patients and colleagues. Despite these disparities, some studies found that females reported equal overall career satisfaction rating with males in ophthalmology, whereas others suggested higher burnout rates. Conclusions and Relevance: Ophthalmology is approaching sex parity, however, the increase in the proportion of females in ophthalmology had not translated to an increase in female representation in leadership positions. Sex disparities persisted across many domains including recruitment, training, practice patterns, academic productivity, and income. Interventions may improve sex equity in the field.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".