Prescription drug insurance and unmet need for health care: a cross-sectional analysis.
Bibliographic record
Abstract
BACKGROUND: Despite Canada's universal health insurance coverage, many Canadians still report an unmet need for health care. I investigated whether not having prescription drug insurance increases the likelihood of reporting an unmet need for health care. I hypothesized that people without prescription drug insurance would be more likely than those with insurance to report an unmet health care need. METHODS: I included 31 630 people in Ontario 64 years of age or younger who had participated in the Canadian Community Health Survey Cycle 3.1. Multivariate logistic regression models were used to obtain an adjusted odds ratio (OR) for the association between having prescription drug insurance and reporting an unmet need for health care in the past 12 months, adjusting for age, sex, socio-economic status, health status and having a regular medical doctor. The reasons for reporting an unmet need for care were stratified into reasons related or not related to prescription drug insurance. Three separate multivariate logistic regressions were performed to obtain an adjusted OR for the association between prescription drug insurance and unmet need based on the reasons for reporting unmet need. RESULTS: Not having prescription drug insurance that covers all or part of prescription medication costs increased the likelihood of reporting an unmet need for health care services (adjusted OR 1.27, 95% confidence interval [CI] 1.16-1.39). Not having such insurance significantly increased the likelihood of reporting an unmet need for health care for reasons that were related to prescription drug insurance (adjusted OR 2.21, 95% CI 1.80-2.71). This relation was not significant when the analysis was restricted to people who reported unmet need for health care for reasons that did not relate to prescription drug insurance (adjusted OR 1.12, 95% CI 1.00-1.23). CONCLUSIONS: These results suggest an association between a lack of prescription drug coverage and reporting an unmet need for health care. This association warrants further investigation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".