National survey of paediatric vision screening programs across Canada: Identifying major gaps and call to action
Bibliographic record
Abstract
OBJECTIVE: Paediatric vision screening programs identify children with ocular abnormalities who would benefit from treatment by an eye care professional. A questionnaire was conducted to assess existence and uptake of school-based vision screening programs across Canada. A supplementary questionnaire was distributed among Ontario's public health units to determine implementation of government mandated vision screening for senior kindergarten children. METHODS: Chief Medical Officers of Health for each province and territory, and Ontario's thirty-four public health units were sent a questionnaire to determine: 1) whether school-based vision screening is being implemented; 2) what age groups are screened; 3) personnel used for vision screening; 4) the type of training provided for vision screening personnel; and 5) vision screening tests performed. RESULTS: Of the thirteen provinces/territories in Canada, six perform some form of school-based vision screening. Two provinces rely solely on non-school-based programs offering eligible children an eye examination by an optometrist and three rely on ocular assessment conducted by a nurse at well-child visits. In Ontario, where since 2018 vision screening for all senior kindergarten students is government mandated, only seventeen public health jurisdictions are implementing universal vision screening programs using a variety of personnel ranging from food safety workers to optometrists. CONCLUSION: Good vision is key to physical and emotional development. There is an urgent need for a universal, evidence-based and cost-effective multidisciplinary approach to standardize paediatric vision screening across Canada and break down barriers preventing children from accessing eye care.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".