The Prevalence of Occupational Burnout Among Ophthalmologists: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective To investigate the prevalence of occupational burnout among ophthalmologists in order to better understand the mental and physical well-being of eye physicians and surgeons in the professional workplace. Study Design A systematic review and meta-analysis. Methods Online computer databases MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software. Data on reported burnout prevalence was extracted. STATA 15.0 was used to conduct meta-analysis. Synthesis: Our search strategy identified 318 records from online databases and 11 records from grey literature search, which were screened at 2-levels. Title and abstracts of each record were screened resulting in 24 records moving to full-text screening. Total of 9 records were utilized for quantitative analysis in the data extraction stage. Our results indicated significant professional burnout among ophthalmologists (ES = 0.41; CI: [0.26, 0.56]) with significant emotional exhaustion (ES = 0.43; CI: [0.33, 0.53]), depersonalization (ES = 0.29; CI: [0.13, 0.46]), and a low sense of personal accomplishment (ES = 0.36; CI: [0.08, 0.63]). Conclusions Significant occupational burnout among ophthalmologists is concerning because burnout can have a negative effect on the physical and mental health of eye physicians and surgeons. It could impact productivity, cutbacks in work hours, or lead to early retirement from the profession. Contributing factors in ophthalmologist burnout including work overload need to be addressed in a timely manner.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".