Prevalence and determinants of visual impairment in Canada: cross-sectional data from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and determinants of visual impairment in Canada. DESIGN: Cross-sectional population-based study. PARTICIPANTS: 30,097 people in the Comprehensive Cohort of the Canadian Longitudinal Study on Aging METHODS: Inclusion criteria included being between the ages of 45 and 85 years old, community-dwelling, and living near one of the 11 data collection sites across 7 Canadian provinces. People were excluded if they were in an institution, living on a First Nations reserve, were a full-time member of the Canadian Armed Forces, did not speak French or English, or had cognitive impairment. Visual acuity was measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart while participants wore their usual prescription for distance, if any. Visual impairment was defined as presenting binocular acuity worse than 20/40. RESULTS: Of Canadian adults, 5.7% (95% CI 5.4-6.0) had visual impairment. A wide variation in the provincial prevalence of visual impairment was observed ranging from a low of 2.4% (95% CI 2.0-3.0) in Manitoba to a high of 10.9% (95% CI 9.6-12.2) in Newfoundland and Labrador. Factors associated with a higher odds of visual impairment included older age (odds ratio [OR] = 1.07, 95% CI 1.06-1.08), lower income (OR = 2.07 for those earning less than $20 000 per year, 95% CI 1.65-2.59), current smoking (OR = 1.52, 95% CI 1.25-1.85), type 2 diabetes (OR = 1.20, 95% CI 1.03-1.41), and memory problems (OR = 1.44, 95% CI 1.04-2.01). CONCLUSIONS: Refractive error was the leading cause of visual impairment. Older age, lower income, province, smoking, diabetes, and memory problems were associated with visual impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".