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Record W4323925072 · doi:10.1177/02646196231158919

Investigating the impact of COVID-19 on individuals with visual impairment

2023· article· en· W4323925072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Visual Impairment · 2023
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial distancePandemicDeclarationSocial isolationGrey literaturePopulationDistressCoronavirus disease 2019 (COVID-19)Visual impairmentInclusion (mineral)Health careIsolation (microbiology)PsychologyMedicineMEDLINEEnvironmental healthPsychiatryPolitical scienceDiseaseClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.409
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it