Feasibility of a school-based vision screening program to detect undiagnosed visual problems in kindergarten children in Ontario
Bibliographic record
Abstract
BACKGROUND: Visual problems can negatively affect visual development and learning but often go undetected. We assessed the feasibility of scaling up a school-based screening program to identify and treat kindergarten children with visual problems. METHODS: We conducted a prospective cohort study offering vision screening to junior (JK) and senior kindergarten (SK) children attending 43 schools in 15 Ontario communities. Screening comprised photoscreeners and tests of visual acuity, stereoacuity and eye alignment. Children who failed any test were referred for a comprehensive eye examination, with treatment as needed (e.g., glasses). RESULTS: < 0.001). Referral rates to an optometrist varied across schools (mean referral rate for children in JK 53%, range 25%-83%; mean referral rate for children in SK 34%, range 12%-61%). Among 4811 children who were screened, a visual problem was detected in 516 (10.7%), including 164 (3.4%) with amblyopia and 324 (6.7%) with clinically significant refractive errors. For 347 (67.2%) of the children with a visual problem, this was their first eye examination. Rescreening in Year 2 did not lead to detection of additional problems among children who passed screening in Year 1. Regardless of location (child's school or optometrist's office), 1563 (68.9%) of children attended the follow-up optometry examination. Most of the children who were surveyed (291 of 322, 90.4%) indicated that they enjoyed vision screening. INTERPRETATION: Many children in Ontario with a visual problem were not being identified by the status quo in 2015-2017. We found that in-school vision screening with follow-up eye examinations is an effective strategy for identifying at-risk children and placing them in eye care before grade 1.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".