Continued care and provision of glasses are necessary to improve visual and academic outcomes in children: Experience from a cluster-randomized controlled trial of school-based vision screening
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of a kindergarten vision screening program by randomly assigning schools to receive or not receive vision screening, then following up 1.5 years later. METHODS: Fifty high-needs elementary schools were randomly assigned to participate or not in a vision screening program for children in senior kindergarten (SK; age 5‒6 years). When the children were in Grade 2 (age 6‒7 years), vision screening was conducted at all 50 schools. RESULTS: Contrary to expectations, screened and non-screened schools did not differ in the prevalence of suspected amblyopia in Grade 2 (8.6% vs. 7.5%, p = 0.10), nor prevalence of other visual problems such as astigmatism (45.1% vs. 47.1%, p = 0.51). There was also no difference between screened and non-screened schools in academic outcomes such as the proportion of children below grade level in reading (33% vs. 29%) or math (44% vs. 38%) (p = 0.86). However, more children were wearing glasses in screened than in non-screened schools (10.2% vs. 7.8%, p = 0.05), and more children reported their glasses as missing or broken (8.3% vs. 4.7%, p = 0.01), suggesting that SK screening had identified successfully those in need of glasses. Examination of individual results revealed that 72% of children diagnosed and treated for amblyopia in SK no longer had amblyopia in Grade 2. CONCLUSION: The prevalence of amblyopia and other visual problems was not reduced in Grade 2 by our SK vision screening program, perhaps because of poor treatment compliance and high attrition. The results suggest that a single screening intervention is insufficient to reduce visual problems among young children. However, the data from individuals with amblyopia suggest that continuing vision care and access to glasses benefits children, especially children from lower socioeconomic class.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".