Significant disparities in eyeglass insurance coverage in Canada
Bibliographic record
Abstract
OBJECTIVE: To describe patterns of access to eyeglass insurance by Canadians. DESIGN: A population-based, cross-sectional survey. PARTICIPANTS: A total of 134 072 respondents to the Canadian Community Health Survey 2003 who were aged ≥12 years. METHODS: We compared self-reported insurance coverage for eyeglasses or contact lenses provided by private, government, or employer-paid plans. RESULTS: Overall, 55.0% of Canadians aged ≥12 years had insurance that covers all or part of the costs of optical correction. School-age children (63.3%) and individuals aged 20-39 years (55.9%) and 40-64 years (59.5%) had higher coverage rates than seniors (aged ≥65 years) (33.8%, p < 0.05). Canadians residing in the 3 territories had the highest coverage (76.9%), while those in Quebec had the lowest coverage (39.1%, p < 0.05). Lower coverage was reported among immigrants (47.3%) versus nonimmigrants (57.4%, p < 0.05), nonwhites (49.2%) versus whites (56.4%, p < 0.05) and aboriginals (70.7%), and the self-employed (38.5%) versus employees (63.8%). Among Canadians in the 20-64 years age group, individuals in the lower or middle income bracket were 40% (prevalence ratio [PR] 0.60, p < 0.05) less likely to have insurance than those in the upper-middle or higher income bracket after adjusting for ethnicity, immigrant status, and education. Compared to those with university or college education, individuals with less than secondary school education were 13% (adjusted PR 0.87, p < 0.05) less likely to have insurance. CONCLUSIONS: Significant disparities exist in eyeglass insurance coverage in Canada. Individuals with low levels of income and education, and the self-employed, seniors, immigrants, nonwhites, and residents of Quebec had less coverage. Studies are needed to understand whether these disparities contribute to the visual impairment burden in Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| 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".