Impact of the COVID-19 Pandemic on Mental Health among Patients with Chronic Ocular Conditions
Bibliographic record
Abstract
The COVID-19 pandemic had significant impacts on the mental and visual health of patients. This cross-sectional, survey-based, multicentric study evaluates the state of mental and visual health among patients with chronic ocular diseases such as glaucoma, neovascular age-related macular degeneration, diabetic retinopathy, or chronic uveitis during the lockdown period of the COVID-19 pandemic. Mental health was assessed using three questionnaires: the Patient Health Questionnaire-9 (PHQ-9), the Impact of Event Scale-Revised (IES-R), and the National Eye Institute Visual Function Questionnaire-25 (VFQ-25). A total of 145 patients completed the questionnaires. The PHQ-9 showed that most respondents (n = 89, 61%) had none or minimal depressive symptoms, while 31 (21%) had mild depressive symptoms, 19 (13%) had moderate depressive symptoms, 5 (3%) had moderately severe depressive symptoms, and 1 (1%) had severe depressive symptoms. Regarding stress surrounding the pandemic, the median IES-R showed mild distress in 16 (11%), moderate distress in 7 (5%), and severe distress in 4 (3%). The COVID-19 pandemic lockdowns had a negative impact on patients’ mental health with close to 20% of the patients reporting at least moderately depressive symptoms and 19% reporting at least mildly distressful symptoms.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".