Disparities in primary and emergency health care among “off-reserve” Indigenous females compared with non-Indigenous females aged 15–55 years in Canada
Bibliographic record
Abstract
BACKGROUND: Access to primary care protects the reproductive and non-reproductive health of females. We aimed to quantify health care disparities among "off-reserve" First Nations, Métis and Inuit females, compared with non-Indigenous females of reproductive age. METHODS: We used population-based data from cross-sectional cycles of the Canadian Community Health Survey (2015-2020), including 4 months during the COVID-19 pandemic. We included all females aged 15-55 years. We measured health care access, use and unmet needs, and quantified disparities through weighted and age-standardized absolute prevalence differences compared with non-Indigenous females. RESULTS: We included 2902 First Nations, 2345 Métis, 742 Inuit and 74 760 non-Indigenous females of reproductive age, weighted to represent 9.7 million people. Compared with non-Indigenous females, Indigenous females reported poorer health and higher morbidity, yet 4.2% (95% confidence interval [CI] 1.8% to 6.6%) fewer First Nations females and 40.7% (95% CI 34.3% to 47.1%) fewer Inuit females had access to a regular health care provider. Indigenous females waited longer for primary care, more used hospital services for nonurgent care, and fewer had consultations with dental professionals. Accordingly, 3.2% (95% CI 0.3% to 6.1%) more First Nations females and 4.0% (95% CI 0.7% to 7.3%) more Métis females reported unmet needs, especially for mental health (data for Inuit females not reported owing to high variability). INTERPRETATION: During reproductive age, Indigenous females in Canada face many disparities in health care access, use and unmet needs. Solutions aimed at increasing access to primary care are urgently needed to advance health care reconciliation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".