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Record W3038524427 · doi:10.17576/mh.2020.1501.21

Comparison of Habitual Visual Acuity and Stereoacuity between Children Attending Kemas and Urban Private Preschools

2020· article· en· W3038524427 on OpenAlexfundno aff
Mohd Izzuddin Hairol

Bibliographic record

VenueMedicine & health · 2020
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
FundersMinistry of Rural Affairs
KeywordsStereoscopic acuityOptometryMedicineVisual acuityPsychologyOphthalmology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.350
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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