Towards a national pre‐school vision screening programme
Bibliographic record
Abstract
The aim of the study is to examine common approaches to pre-school vision screening, including the current New South Wales model which is known as Statewide Eyesight Preschooler Screening (StEPS) to determine whether the criteria set by the World Health Organization are fulfilled by StEPS, and therefore, whether there is sufficient justification to deploy a similar model nationally. A literature review was conducted to answer four key questions related to vision screening. The authors sought to review (i) the justification for vision screening of a pre-school population; (ii) the principles and best approaches to vision screening such as how, where and who should conduct vision screening; (iii) the conditions that are targeted in vision screening; and (iv) the acceptable pass/fail vision screening criteria. The StEPS vision screening model is an accurate, reliable and economical way of screening for reduced vision at a time when neural plasticity allows improvement in vision following intervention. It fulfils World Health Organization criteria for a successful screening programme. StEPS has very high participation rates compared to other screening methods in Australia due to its unique model whereby screeners utilise early childhood settings to recruit and screen 4-year-old children. Due consideration should be given to deploying the StEPS vision screening model nationally.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".