Unmasking population undercounts, health inequities, and health service access barriers across Indigenous populations in urban Ontario
Bibliographic record
Abstract
OBJECTIVES: Our Health Counts (OHC) methods are designed to address gaps in urban-based Indigenous health information. In partnership with local Indigenous health service providers, we have successfully implemented OHC in six Ontario cities. The aim of this study is to summarize findings regarding Indigenous population undercount, health inequities, and health service access barriers across study sites. METHODS: We estimated Indigenous population size using OHC census participation survey responses and a multiplier approach. Health inequities between Indigenous populations and overall populations in each city were examined using respondent-driven sampling (RDS), adjusted OHC survey results, and existing public data. Measures included health status outcomes; determinants of health; barriers to health service access, including discrimination by health service providers; and unmet health needs. RESULTS: Indigenous social networks were strong and extensive, and the urban populations demonstrate resilience and cultural continuity across multiple measures. Self-reported rates of census participation for Indigenous populations were markedly lower than those for the general population in each city, and OHC Indigenous population size estimates were consistently 2‒4 times higher than reported in the census. Indigenous to general population health inequities cut across measures of chronic disease, determinants of health, and unmet health needs. Indigenous populations experienced multiple barriers to health services access, including racial discrimination by health service providers. CONCLUSION: The Canadian census appears to markedly underestimate Indigenous population size in urban areas. Indigenous health inequities and service access barriers are striking and cross-cutting. Timely adaptation of health policies, services, and funding allocations in response to these findings is recommended.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".