Prevalence of health anxiety in indian ophthalmologists during COVID-19: a survey
Bibliographic record
Abstract
Introduction Mental health concerns are common in health care workers during pandemic. There are no studies of the prevalence of health anxiety in ophthalmologists in India. Objectives To estimate the prevalence of health anxiety in ophthalmologists practicing in India during the ongoing pandemic. Methods A questionnaire-based online survey on the “changes and challenges during COVID-19” using Google forms was sent to all members of the All India Ophthalmological Society. Besides demographics, the survey had questions to assess the general mental and medical health status of the ophthalmologists. Short Health Anxiety Inventory (SHAI) was used to assess health anxiety. Results 1027 ophthalmologists responded to the study. Higher stress was experience by 83.1% compared to pre-COVID while examining patients closely (35.9%) or during surgery due to the risk of aerosol generation (29.3%). SHAI score >20 was observed in 5.6%. Only emergency services were being provided by 50% and 17% in the SHAI > 20 group were not working as compared to overall 14%. Conclusions Our findings indicate that a majority of the ophthalmologists were under stress during the COVID-19 pandemic but only a small proportion experienced health anxiety. It is likely that mental health issues may arise among ophthalmologists in the foreseeable future.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".