Psychological effects of the pandemic on vision impairment patients
Why this work is in the frame
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Bibliographic record
Abstract
Our study aims to understand the impact of the Coronavirus Disease of 2019 (COVID-19) pandemic on mental health of individuals with vision impairment and to highlight the unique challenges faced due to social isolation and disruption in healthcare services. The study design is a systematic review and meta-analysis. A literature search was conducted using MEDLINE, EMBASE, and CINAHL databases. A total of 363 articles were screened, 18 studies were included for qualitative analysis and 12 were used for quantitative analysis. After screening, a risk of bias assessment was carried out. Data were extracted and a meta-analysis was performed using STATA 14.0. Fixed-effect and random-effect models were computed based on heterogeneity. Our meta-analysis encompassed 16 studies investigating the psychological impact of COVID-19 in 2317 vision loss patients. The meta-analysis indicated significant levels of loneliness (44%, 95% confidence interval [CI] = [0.24 to 0.64]); anxiety (45%, 95% CI = [–0.31 to 1.21]); depression (48% CI = [–0.05 to 1.01]); fear of vision loss (42% mild, 95% CI = [0.24 to 0.61]); fear of contracting COVID-19 (61%, 95% CI = [0.45 to 0.77]); and psychiatric disorders (28%, 95% CI = [0.07 to 0.50]) for patients with vision impairment. Vision loss patients experienced significant levels of loneliness, anxiety, depression, fear of vision loss, fear of contracting COVID-19, and psychiatric disorders during the pandemic. This psychological distress is attributable to poor access to health care, a lack of social support, and difficulties adhering to pandemic-related precautions such as physical distancing and avoiding contaminated surfaces.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it