The impact of physician-patient ethnic concordance and discordance on the health of black and indigenous patients: A systematic review of the literature
Bibliographic record
Abstract
Health disparities persist among Black and Indigenous communities in Canada, despite national efforts to promote equity, diversity, and inclusion. These disparities are particularly pronounced in the management of chronic diseases and mental health conditions. Ethnic concordance and discordance may influence health outcomes. This systematic review aims to evaluate the impact of patient–physician ethnic concordance and discordance on the management of chronic diseases and mental health conditions among Black and Indigenous patients. This review followed the PRISMA guidelines and was registered with PROSPERO. A university librarian conducted a comprehensive literature search. Eligible studies included peer-reviewed quantitative, qualitative, and mixed-method studies. All screening stages were performed independently by four reviewers in COVIDENCE, with discrepancies resolved by a separate author. Of 6826 records screened, 24 studies were included (22 on Black patients, two on Indigenous patients). Evidence suggested that concordant care improved select outcomes, including blood pressure control, medication adherence, HIV testing uptake, pain assessment, and referrals to traditional healing. Interpersonal outcomes such as communication, trust, and perceptions of providers were also consistently stronger in concordant relationships. However, effects on disease prevention, monitoring, and overall clinical outcomes were largely inconsistent. The small number of studies limited findings for Indigenous populations. The overall impact of ethnic concordance on chronic disease and mental health management appears limited. Our findings do not demonstrate a consistent association between concordance and clinical outcomes. Addressing health inequities may require broader systemic and structural interventions, including improving access to high-quality care, mitigating institutional bias, and strengthening the capacity of minority-serving healthcare settings.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".