Determinants of Health Care Needs in Relation to Vision Correction among Adolescents in the United Arab Emirates: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Uncorrected refractive error has been suggested to affect children's development, educational performance, and socialization. Sociodemographic and environmental differences among individuals may impact their accessibility to utilizing appropriate services, impacting their vision-dependent activities. METHODS: Guided by the population health framework, this retrospective study assessed the prevalence of self-reported vision correction needs and its determinants for a sample of adolescents (n = 6363) from the United Arab Emirates (UAE) aged 13 to 20 years between 2007 to 2009. RESULTS: The findings suggest a relatively high prevalence of self-reported vision correction needs (26.8%), with among 24.8% males and 31.7% among females. Factors that were significantly associated with vision correction needs included age, biological sex, location of residence (emirate), nationality, parental education and employment level, household financial status, screen time use, visiting an eye specialist in the past year, and daily functional capacity. CONCLUSION: Reporting the vision correction needs of the adolescent population and identifying its determinants may help identify and resolve modifiable barriers to accessing the appropriate vision care resources. Further research in assessing the type of refractive error, potential genetic and environmental factors, and vision care services in each emirate may help decision-makers set appropriate policies to improve the overall quality of eye health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".