Health and Prescription Drug Coverage Inequity: Towards Inclusive Migration and Health Policy
Bibliographic record
Abstract
Health financing policies implemented by nations around the world vary based on who receives coverage and what health system resources are covered. Although, many health systems are attempting to move towards Universal Health Coverage, part of their populations continue to incur out-of-pocket payments for using all or some health services. Some health systems restrict health insurance for certain migrant populations, providing coverage for emergency care only, or none at all. Other health systems fail to provide coverage for prescription drugs, leaving those without the ability to pay out-of-pocket for medications behind. The lack of financial protections against catastrophic or impoverishing healthcare expenditures for these patients may deter them from seeking the care they need or increase the risk of severe financial hardships. This dissertation addresses these migrant and drug coverage gaps by examining the impacts of health financing policies and how these can be changed to move health systems towards Universal Health Coverage. First, this dissertation examines restrictions to refugee health policy in Canada by conducting an interpretive policy analysis to reveal how political actors strategically use causal stories to enact policy change. Second, quantitative studies assessing the effects of health insurance on migrants’ health-related outcomes are systematically reviewed. Third, this dissertation explores a provincial health system without universal prescription drug coverage to establish associations between health services use, prescription drug coverage and immigrant category. Finally, given migrants experience health outcome and health services utilization disparities, an exploratory analysis of factors that impede or assist migrants’ access to prescription drugs is conducted to uncover how these factors influence their health. While each study is distinct, together, these chapters build on each other using mixed methodological approaches to identify ways that address health financing policy gaps to reduce health inequities, build inclusive and cost-effective health systems and strengthen global health security.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".