Unmet health needs and discrimination by healthcare providers among an Indigenous population in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVES: Inequalities between Indigenous and non-Indigenous peoples in Canada persist. Despite the growth of Indigenous populations in urban settings, information on their health is scarce. The objective of this study is to assess the association between experience of discrimination by healthcare providers and having unmet health needs within the Indigenous population of Toronto. METHODS: The Our Health Counts Toronto (OHCT) database was generated using respondent-driven sampling (RDS) to recruit 917 self-identified Indigenous adults within Toronto for a comprehensive health assessment survey. This cross-sectional study draws on information from 836 OHCT participants with responses to all study variables. Odds ratios and 95% confidence intervals were estimated to examine the relationship between lifetime experience of discrimination by a healthcare provider and having an unmet health need in the 12 months prior to the study. Stratified analysis was conducted to understand how information on access to primary care and socio-demographic factors influenced this relationship. RESULTS: The RDS-adjusted prevalence of discrimination by a healthcare provider was 28.5% (95% CI 20.4-36.5) and of unmet health needs was 27.3% (95% CI 19.1-35.5). Discrimination by a healthcare provider was positively associated with unmet health needs (OR 3.1, 95% CI 1.3-7.3). CONCLUSION: This analysis provides new evidence linking discrimination in healthcare settings to disparities in healthcare access among urban Indigenous people, reinforcing existing recommendations regarding Indigenous cultural safety training for healthcare providers. Our study further demonstrates Our Health Counts methodologies, which employ robust community partnerships and RDS to address gaps in health information for urban Indigenous populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".