Our health counts: population-based measures of urban Inuit health determinants, health status, and health care access
Bibliographic record
Abstract
OBJECTIVE: Health determinants and outcomes are not well described for the growing population of Inuit living in southern urban areas of Canada despite known and striking health disparities for Inuit living in the north. The objective of this study was to work in partnership with Tungasuvvingat Inuit (TI) to develop population prevalence estimates for key indicators of health, including health determinants, health status outcomes, and health services access for Inuit in Ottawa, Canada. METHODS: We employed community-based respondent driven sampling (RDS) and a comprehensive health assessment survey to collect primary data regarding health determinants, status, and service access. We then linked with datasets held by the Institute for Clinical Evaluative Sciences (ICES), including hospitalization, emergency room, and health screening records. Adjusted population-based prevalence estimates and rates were calculated using custom RDS software. RESULTS: We recruited 341 Inuit adults living in Ottawa. The number of Inuit living, working or accessing health and social services in the City of Ottawa was estimated to be 3361 (95% CI 2309-4959). This population experiences high rates of poverty, unemployment, household crowding, and food insecurity. Prevalence of hypertension (25%; 95% CI 18.1-33.9), chronic obstructive pulmonary disease (6.7%; 95% CI 3.1-10.6), cancer (6.8%; 95% CI 2.7-11.9), and rates of emergency room access were elevated for Inuit in Ottawa compared to the general population. Access to health services was rated fair or poor by 43%. Multiple barriers to health care access were identified. CONCLUSIONS: Urban Inuit experience a heavy burden of adverse health determinants and poor health status outcomes. According to urban Inuit in Ottawa, health services available to Inuit at the time of the study were inadequate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".