Work satisfaction, burnout and gender-based inequalities among ophthalmologists in India: A survey
Bibliographic record
Abstract
BACKGROUND: Ophthalmology is a rapidly evolving branch of medicine and advancing technology has raised the bar of patient expectations and outcomes. However, studies that assess physician stress and satisfaction especially in developing countries are limited in literature. OBJECTIVE: This index study aims at looking at the levels of job satisfaction, burnout and perception of gender disparity among ophthalmologists in India. METHODS: An Internet-based survey was sent out to ophthalmologists. 297 respondents replied with responses, which were anonymized and analyzed. RESULTS: Of the 297 respondents, 101 were female and 196 were male ophthalmologists. The mean duration of practice of the respondents was 14.66 years. 54.21% (161/297) responded affirmatively when asked if they were satisfied with their careers. 19% (56/297) were not satisfied. 26.94% (80/297) replied that although they were satisfied, they wished they had more time for family. A quarter (25.2%; 63 out of 250) of the respondents felt burnt-out at that stage of their careers. 68.35% (203/297) of the respondents felt that being a woman ophthalmologist in India was more challenging than being a male ophthalmologist. This perception was significantly more amongst women respondents (p < 0.0002). Greater family responsibility, long working hours, and having to work harder were the challenges faced by female ophthalmologists. There was a significant difference in perception between male and female ophthalmologists regarding the presence of disparity in earnings given equal qualifications and experience with more women responding in the affirmative. CONCLUSIONS: Indian ophthalmologists have personally and professionally satisfying careers with low rates of burnout. While good family support and an understanding partner help ophthalmologists achieve good work-life balance, women ophthalmologists perceive a gender-based disparity when it comes to proving their worth and getting suitably remunerated.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".