Determinants of Myopia Severity Based on Behavior and Genetic Factors
Bibliographic record
Abstract
General Background: Myopia is a leading global vision disorder affecting quality of life and projected to impact over half the world’s population by 2050. Specific Background: Among university students, behavioral factors such as prolonged near work and gadget use, alongside genetic predisposition, are recognized contributors, yet findings remain inconsistent, particularly in public health student populations. Knowledge Gap: Limited studies have specifically identified dominant determinants of myopia severity in public health students, a group expected to champion health promotion. Aims: This study aimed to determine the behavioral and genetic factors most strongly associated with myopia severity among Public Health students at Universitas Prima Indonesia. Results: Using a cross-sectional analytic design with 74 diagnosed myopic students, multivariate logistic regression revealed significant associations with reading distance <30 cm, gadget use >3 hours/day, and family history, with genetic factors showing the highest odds ratio (AOR = 8.567, p = 0.002). Gender was not significant. Novelty: The study uniquely combines primary and secondary data, integrates multivariate analysis, and aligns findings with the Ottawa Charter for Health Promotion and SDG 3. Implications: Results support campus-based preventive strategies, including visual hygiene education, gadget use reduction, and early screening for genetically at-risk students.Highlight : Genetic factors are the most dominant determinant of the degree of myopia. Hours of gadget use and close reading increase the risk of myopia. Health promotion on campus is important for preventing the progression of myopia. Keywords : Myopia, Derajat Myopia, Mahasiswa Kesehatan Masyarakat, Penggunaan Gadget, Faktor Genetik
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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 | 0.000 | 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) | 0.003 | 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".