Indigenous people’s experiences of primary health care in Canada: a qualitative systematic review
Bibliographic record
Abstract
INTRODUCTION: Indigenous people in Canada encounter negative treatment when accessing primary health care (PHC). Despite several qualitative accounts of these experiences, there still has not been a qualitative review conducted on this topic. In this qualitative systematic review, we aimed to explore Indigenous people's experiences in Canada with PHC services, determine urban versus rural or remote differences and identify recommendations for quality improvement. METHODS: This review was guided by the Joanna Briggs Institute's methodology for systematic reviews of qualitative evidence. MEDLINE, CINAHL, PubMed, PsycInfo, Embase and Web of Science as well as grey literature and ancestry sources were used to identify relevant articles. Ancestry sources were obtained through reviewing the reference lists of all included articles and determining the ones that potentially met the eligibility criteria. Two independent reviewers conducted the initial and full text screening, data extraction and quality assessment. Once all data were gathered, they were synthesized following the meta-aggregation approach (PROSPERO CRD42020192353). RESULTS: The search yielded a total of 2503 articles from the academic databases and 12 articles from the grey literature and ancestry sources. Overall, 22 articles were included in this review. Three major synthesized findings were revealed-satisfactory experiences, discriminatory attitudes and systemic challenges faced by Indigenous patients-along with one synthesized finding on their specific recommendations. CONCLUSION: Indigenous people value safe, accessible and respectful care. The discrimination and racism they face negatively affect their overall health and well-being. Hence, it is crucial that changes in health care practice, structures and policy development as well as systemic transformation be implemented immediately.
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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 | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".