Comparison of Habitual Visual Acuity and Stereoacuity between Children Attending Kemas and Urban Private Preschools
Bibliographic record
Abstract
The assessment of a preschooler's visual status is important as it forms part of the measure to assess the child's school readiness.However, not all children attending preschools have equal opportunity to undergo vision screening programmes.In this study, we measured presenting habitual near and distance visual acuity and stereoacuity in 6-year-old children (n=385).These parameters were measured in and compared between preschoolers attending urban, privately-run kindergartens and those attending KEMAS preschools, which were typically from suburban and rural areas with families of very low income.Seven percent of KEMAS preschoolers failed the distance visual acuity test while the failure rate for private preschoolers was 6.0%.For near visual acuity, a higher percentage of private preschoolers failed the test (8.7%)than KEMAS preschoolers (4.9%).A slightly higher percentage of private preschoolers had weak stereopsis (3.3%) compared to KEMAS preschoolers (2.5%).However, the differences found between the two preschooler groups were not statistically significant (all p>0.05).The proportion of children who failed each of the screening criteria for distance vision, near vision, and stereopsis was similar between KEMAS and private preschools.Therefore, an universally inclusive vision screening programme should be conducted for all preschool types to detect, diagnose, treat, and potentially prevent any visual impairment.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".