The barriers to healthcare access and their impact on health outcomes for people medically uninsured in Canada: A scoping review
Bibliographic record
Abstract
Objective: The objective of the scoping review was to summarize the current literature on the barriers to healthcare access and its impact on health outcomes for people who are medically uninsured (including those with precarious or no immigration status) in Canada; identify gaps in the existing knowledge; and highlight implications for practice and policy. Methods: We undertook a systematic search in the electronic databases of PsycInfo, MEDLINE (Ovid), CINAHL, ProQuest Nursing and Allied Health, Web of Science and ProQuest Dissertations and Theses Global. Inclusion criteria consisted of articles published in English reporting information about barriers to healthcare access or its impact on health among people with precarious immigration status in Canada. Relevant themes emerging from the included studies were reviewed by the research team and organized into three levels of barriers to accessing healthcare. Results: Ten articles were included in this scoping review. Our review found that individuals living with precarious or no immigration status and being denied public health insurance experienced significant obstructions to accessing healthcare services, the result of policy, interpersonal and intrapersonal-level barriers. These barriers include ineligibility for provincial or federal healthcare insurance, negative interactions with healthcare providers, fear of interacting with public services and lack of sufficient income to pay for out-of-pocket healthcare expenses. This resulted in unmet healthcare needs, negative and worsening health outcomes, avoidance of formal healthcare institutions and reliance on emergency care services caused by delays in help-seeking. Conclusions: People who are medically uninsured in Canada experience avoidable negative health outcomes, including higher risk of death because of barriers to access to healthcare. Provincial healthcare insurance should be available for these vulnerable groups to avoid unnecessary health problems and complications and to reduce the burden on the healthcare system. New (im)migrants should receive education on their right to receive health services, and healthcare providers must receive training on how to best support the health and wellbeing of this population.
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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 | 0.016 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.034 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".