Patient-Reported Experiences in Accessing Primary Healthcare among Immigrant Population in Canada: A Rapid Literature Review
Bibliographic record
Abstract
(1) Background: Immigrants represent around 21.9% of the total population in Canada and encounter multifaceted obstacles in accessing and receiving primary healthcare. This literature review explores patient experiences in primary care from the perspective of immigrants and identifies areas for further research and improvement. (2) Methods: A comprehensive search was performed on PubMed, MEDLINE, Embase, SCOPUS, and Google scholar to identify studies published from 2010 to July 2020. Relevant articles were peer-reviewed, in English language, and reported patient experiences in primary healthcare in Canada. (3) Results: Of the 1566 searched articles, 19 articles were included in this review. Overall, the finding from articles were summarized into four major themes: cultural and linguistic differences; socioeconomic challenges; health system factors; patient-provider relationship. (4) Conclusion: Understanding the gaps to accessing and receiving appropriate healthcare is important to shape policies, enhance the quality of services, and deliver more equitable healthcare services. It is therefore pertinent that primary healthcare providers play an active role in bridging these gaps with strong support from policymakers. Understanding and respecting diversity in culture, language, experiences, and systems is crucial in reducing health inequalities and improving access to quality care in a respectful and responsive manner.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.023 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".