Psychological effects of the pandemic on vision impairment patients
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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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".