Out-of-School Vision Screening in North India: Estimating the Magnitude of Need
Bibliographic record
Abstract
Purpose: Few studies have examined the extent to which school-based vision screening is sufficient to achieve universal coverage among school-aged children in India.Method: A rural administrative region (‘Block’) was examined. Government records provided the total population of the rural Block, the proportion of school-aged children, and school authorities in the Block provided the number of enrolled students. Absenteeism was measured directly by visiting a representative sample of the schools. The proportion of the school age population found in school was assessed using the indicator, Effective Coverage (EC): the proportion of children attending school divided by the total population of school-aged children in the region.Results: In the rural block, the proportion of children actually enrolled in school was 52% of the school-aged population, with 68% of them attending school. Therefore, EC was 35% (68% of the 52% enrolled).Conclusion: Population coverage by school vision screening would be unacceptably low in a rural setting in northern India. Out-of-school vision screening programs are needed in these rural settings to achieve universal coverage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".