The association between visual impairment, educational outcomes, and mental health: insights from eyeglasses usage among junior high school students in rural China
Bibliographic record
Abstract
This study examined the association between visual impairment, visual impairment corrected by wearing eyeglasses, academic performance, and mental health among junior high school students in rural China. Visual acuity assessments were conducted on 19,425 junior high school students by trained medical and research professionals to determine the presence of visual impairment. All sample students were surveyed with a questionnaire that asked about individual and family characteristics, eyeglasses ownership, and educational aspirations and included a standardized math test. Students then completed an official Chinese simplified version of the Strengths and Difficulties Questionnaire (SDQ) to assess their mental health multidimensionally. Among our sample, 37.2% of them suffered from visual impairment, but only 43.4% of the visually impaired students wore proper eyeglasses. Approximately 9.3% of students were categorized as high risk for mental health problems based on their SDQ score. A significant positive association existed between impaired vision and poorer mental health, and eyeglasses usage was associated with better mental health among visually impaired students. For students with better academic performance, eyeglasses usage associated with better mental health. Eyeglasses usage shows a positive association with higher education aspiration both for students with better and worse academic performance. The significant positive relationship between eyeglasses usage and mental health may guide future interventions and policies designed to improve student mental health by supplying them with eyeglasses.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".