Socioeconomic differences in prescription drug supplemental coverage in Canada: A repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Efforts to achieve universal healthcare coverage are fraught with challenges, not only in low- and middle-income countries but also in high-income ones. Canada, for example, is the only high-income country with universal health insurance that does not include universal coverage for prescription drugs. We first described the extent to which Canadians reported supplementary drug insurance coverage (public or private). Second, we examined associations between individuals' socioeconomic and demographic characteristics and self-reported drug insurance coverage. METHODS: We used logistic regressions and repeated cross-sectional data from two national (2015, 2016) and six Ontario (2005, 2008, 2013-2016) cycles of the Canadian Community Health Survey. RESULTS: We found large socioeconomic differences in the reporting of prescription drug insurance coverage. Individuals of lower socioeconomic status had higher odds of reporting public drug coverage or no coverage while those of higher socioeconomic status had higher odds of reporting private coverage. Respondents' reports indicated that public drug plans were more likely to cover those in poorer health while private plans were more likely to cover those in very good or excellent health. We also documented substantial underreporting of public drug coverage. which may also have access implications. INTERPRETATION: Both the lack of prescription drug insurance and misunderstandings about one's insurance coverage point to limits in Canada's drug insurance system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".