Computer vision syndrome among students during remote learning periods: harnessing digital solutions for clear vision
Bibliographic record
Abstract
Aim: This study aimed to assess the prevalence of Computer Vision Syndrome (CVS) among children and adolescents in Qatar during the period of remote learning and explore the associated factors and discuss some digital health remedies that might reduce the risk. Methods: We conducted an analytical cross-sectional study between June and August 2022 by collecting data via telephone interviews with parents of selected students utilizing the Computer Vision Syndrome Questionnaire (CVS-Q). Results: We completed 1,546 interviews. The mean age of the students was (11 ± 2), male: female ratio was almost 1:1. About one quarter (368, 23.8%) of parents reported a previous diagnosis of visual disturbances among their children with over 88% of them wearing eyeglasses or medical contact lenses. The prevalence of CVS in our sample was about 8% (95%CI: 6.8-9.6). Mother's employment, having positive history of visual disturbances, and excess screen time were found to be significant predictors of CVS. Conclusion: Health care providers in collaboration with teachers should provide parents with evidence-based strategies to prevent or minimize the digital eye strain among students. In the landscape of remote learning, the implementation of digital remedies emerges as a proactive approach to mitigate the risk of digital eye strain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".