The impact of Sanctuary City Policies on healthcare access for immigrants in Winnipeg
Bibliographic record
Abstract
This study examines the impact of Sanctuary City Policies (SCPs) on healthcare access for immigrants with precarious status in Winnipeg, focusing on undocumented immigrants, asylum-seekers, and those awaiting legal status determination. It delves into the challenges these individuals face in accessing healthcare due to legal barriers, insurance availability, and fear of deportation, intensified by their heightened health risks and inequities, especially during crises like the COVID-19 pandemic. The paper analyzes Canada's health rights framework, including international obligations under the International Covenant on Economic, Social and Cultural Rights (ICESCR) and the 1951 Convention Relating to the Status of Refugees, as well as Winnipeg's Newcomer Welcome and Inclusion Policy (NWIP) and different SCPs experiences both abroad and in Canada. It argues that Canada's SCPs, while aiming to protect undocumented immigrants, fall short of fully addressing the obligations outlined in international treaties, leading to disparities in healthcare access. This research integrates international human rights law, international refugee law, and public policy to propose a more inclusive and effective healthcare strategy for immigrants with precarious status, emphasizing the need for a unified approach that overcomes the legal and jurisdictional complexities of Canada's decentralized healthcare 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".