Health Insurance Coverage and Use of Eye Care Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it