Paediatric vision screening in the primary care setting in Ontario
Bibliographic record
Abstract
OBJECTIVES: Early intervention is critical to prevent treatable causes of vision loss in children. The objectives of the current study are: (1) to assess how well primary care physicians in Ontario follow the vision screening guidelines for children as recommended by the Canadian Paediatric Society and the Rourke Baby Record and (2) to identify barriers to vision screening in the primary care setting. DESIGN: Cross-sectional survey. METHODS: A 19-question survey was mailed out to 1000 randomly selected family physicians (family MDs), 1000 general practitioners (GPs) and 1000 paediatricians in Ontario as listed in the 2013 Canadian Medical Directory. RESULTS: A total of 719 completed surveys were included in the analysis (449 from family MDs/GPs and 270 from paediatricians). Vision screening was reported to be performed by 65% of family MDs/GPs and 52% of general paediatricians at every well child visit. While red reflex was reported to be checked by 94% of all physicians in children under 3, it was only performed by 25% of respondents for children over 3. Thirty seven percent of all physicians reported never performing a visual acuity test in any age group. When asked about the obstacles preventing them from performing vision screening, lack of training (family MDs/GPs: 50%, paediatricians: 42%), time constraints (family MDs/GPs: 42%; paediatricians: 40%) and inadequate reimbursement (family MDs/GPs: 17%; paediatricians: 15%) were the most commonly cited reasons. CONCLUSIONS: Strategies to improve vision screening are necessary given that early intervention is crucial to prevent treatable causes of vision loss in children.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".