Investigating the impact of COVID-19 on individuals with visual impairment
Bibliographic record
Abstract
We present a comprehensive review of the various challenges that individuals with visual impairment (VI) face during the COVID-19 pandemic. A structured review was done using online databases PubMed, EMBASE, and grey literature databases between 19 April 2021 and 4 August 2021, using search terms ‘COVID-19’, ‘SARS-CoV-2’, ‘Coronavirus’, or ‘pandemic’ combined with ‘visually impaired’, ‘visual impairment’, or ‘Blind’. Studies included were written in English, published after the World Health Organization (WHO) declaration of the COVID-19 Pandemic (11 March 2020), and focused on the VI population during the pandemic. The initial search yielded 702 publications, of which 20 met our inclusion criteria and were included in analysis. Emotional distress from deteriorating mental health and social isolation were considerably higher in the VI population. For a community that relies on spatial awareness and touch, regulations related to social distancing and avoiding contact were considerable barriers. Further challenges were noted in accessing healthcare, care, receiving timely health information and changes in regulations, adequately sanitizing, using technology, and completing activities of daily living. In the unprecedented times of the COVID-19 pandemic, the VI community has faced unique challenges. A more holistic and inclusive approach needs to be adopted to ensure that more vulnerable populations are adequately cared for.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".