Effects of Remote Learning during the COVID-19 Lockdown on Children’s Visual Health: A Systematic Review (P12-9.007)
Bibliographic record
Abstract
<h3>Objective:</h3> This study aimed to identify the impact of remote learning during the COVID-19 lockdown on children’s visual health. <h3>Background:</h3> Increased exposure to digital devices as part of online classes increases susceptibility to visual impairments, particularly among school students taught using e-learning strategies. <h3>Design/Methods:</h3> A systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data was obtained from Scopus, PubMed and ScienceDirect databases from the year 2020 onwards. We included cross-sectional, case–control, cohort studies, case series and case reports, published in English, Spanish or French, that approached the effects of remote learning during the COVID-19 lockdown on visual health in neurotypical children. We included a total of 21 articles with previous quality assessments using the Joanna Briggs checklist. Risk of bias assessment was applied using the National Institutes of Health quality assessment tool for before-and-after studies with no control group; the tool developed by Hoy et al to assess cross-sectional studies; the Murad et al tool to evaluate the methodological quality of case reports and case series; and the Newcastle-Ottawa Scale for cohort studies. <h3>Results:</h3> All but one study reported a deleterious impact of the COVID-19 lockdown on visual health in children. Overall, the most frequently identified ocular effects were refractive errors, accommodation disturbances and visual symptoms such as dry eye and asthenopia. <h3>Conclusions:</h3> Increased dependence on digital devices for online classes has either induced or exacerbated visual disturbances, such as rapid progression of myopia, dry eye and visual fatigue symptoms, and vergence and accommodation disturbances, in children who engaged in remote learning during the COVID-19 lockdown. <b>Disclosure:</b> Dr. Cortes-Albornoz has nothing to disclose. Ms. Ramirez Guerrero has nothing to disclose. Dr. ROJAS CARABALI has nothing to disclose. Prof. De la Torre Cifuentes has nothing to disclose. Dr. Talero-Gutierrez has nothing to disclose.
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How this classification was reachedexpand
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".