A Comparative Policy Analysis of Health Inequities in Access to Healthcare Across Low- and High-Income Contexts: The Cases of Pakistan and Canada
Bibliographic record
Abstract
Globally, poverty remains a major obstacle to health parity, compromising well-being. This policy analysis aims to examine how poverty affects health inequities and healthcare access in two contexts: Canada, a high-income nation, and Pakistan, a low-income nation. This study employs a grounded approach, integrating a thorough review of the existing critical literature using systematic thematic analysis and synthesis. In Pakistan, chronic underinvestment, rural-urban gaps, inadequate infrastructure, and political instability exacerbate inequities in access to healthcare. Limited coverage, ineffective administrative processes, and gaps in rural healthcare delivery impede growth despite encouraging programs like the Sehat Card and the Ehsaas Program. Conversely, universal healthcare in Canada has lowered financial obstacles to access, but low-income and Indigenous communities are still impacted by service gaps, particularly in dental care, pharmacare, and mental health. Although child poverty rates have been significantly reduced by programs like the Canada Child Benefit, Indigenous children continue to endure disproportionate health risks. Findings underscore a need for equity-driven changes: Pakistan must expand rural health infrastructure and legislate health equity, while Canada should extend coverage to essential but excluded services. Findings underscore the intersecting nature of inequities driven by poverty, gender, geography, and systemic exclusion that highlight opportunities for cross-context policy learning. Canada's equity monitoring frameworks could strengthen Pakistan's health data systems, while Pakistan's community-based Lady Health Worker program offers scalable grassroots models relevant for marginalized Canadian regions. Both countries must prioritize poverty alleviation as a health intervention, integrating justice, sustainability, and accountability to advance global health equity.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".