Unforeseen Digital Eye Strain of Children : A Review
Bibliographic record
Abstract
People of all ages are using mobile devices more frequently, and more children are reportedly using digital media as well, which raises the risk of Digital Eye Strain (DES). There have been few studies on how often DES affects children particularly post-pandemic. The purpose of this study is to review published literature concerning DES, including its pathogenesis and therapy options. A literature search was performed based on PubMed, EMBASE and Scopus databases published from 2003 to 2023 using the broad search term “digital eye strain”, “ocular asthenopia secondary to digital gadgets”, “computer vision syndrome”, “eye strain post-computer or mobile use”, “visual weariness”, and “children" in all fields. Of the 163 articles retrieved, 107 were retained for inclusion in this review. The result reveals that there is an urgent need to inform parents, caregivers, and youth about setting screen time limits and applying ergonomic practices due to the recent surge in digital electronic gadget usage among kids and young adults.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".