Bibliographic record
Abstract
OBJECTIVE: To compare realized access or use of eye care services in adults with self-reported vision problems in Canada and the United States. METHODS: Using the Joint Canada/United States Survey of Health, we examined the differences in use of eye care services in 2018 Canadian respondents and 2930 American respondents with self-reported vision problems. We performed multivariate logistic regression analyses to estimate the probability that individuals with vision problems and various insurance categories would visit an eye care professional. RESULTS: Approximately 8.2% of Americans with self-reported vision problems did not have health insurance. Americans without health insurance had the lowest age-adjusted rate of use of eye care services (42%) compared with Americans with private health insurance (67%) or public health insurance (55%) and Canadians (56%). The difference in use of eye care services between Americans without health insurance and Canadians narrowed when adjusted for income level and was almost eliminated when adjusted for having optional vision insurance. Individuals with optional vision insurance and those with higher income levels were more likely to use eye care services. CONCLUSIONS: Americans with vision problems who had health insurance accessed eye care services at a rate higher than or equal to that of their Canadian counterparts. The gap in access between Canadians and Americans without health insurance narrowed after adjustments for income level and optional vision insurance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".