Birth Outcomes and Infant Mortality by the Degree of Rural Isolation Among First Nations and Non-First Nations in Manitoba, Canada
Bibliographic record
Abstract
CONTEXT: It is unknown whether rural isolation may affect birth outcomes and infant mortality differentially for Indigenous versus non-Indigenous populations. We assessed birth outcomes and infant mortality by the degree of rural isolation among First Nations (North American Indians) and non-First Nations populations in Manitoba, Canada, a setting with universal health insurance. METHODS: A geocoding-based birth cohort study of 25,143 First Nations and 125,729 non-First Nations live births to Manitoban residents, 1991-2000. Degree of rural isolation was defined by an indicator of urban influence (no, weak, moderate/strong) based on the percentage of the workforce commuting to urban areas. FINDINGS: Preterm birth and low birth weight rates were somewhat lower in all rural areas regardless of the degree of isolation as compared to urban areas for both First Nations and non-First Nations. Infant mortality rates were not significantly different across areas for First Nations (10.7, 9.9, 7.9, and 9.7 per 1,000 in rural areas with no, weak, moderate/strong urban influence, and urban areas, respectively), but rates were significantly lower in less isolated areas for non-First Nations (7.4, 6.0, 5.6, and 4.6 per 1,000, respectively). Adjusted odds ratios showed similar patterns. CONCLUSIONS: Living in less isolated areas was associated with lower infant mortality only among non-First Nations. First Nations infants do not seem to have similarly benefited from the better health care facilities in urban centers, suggesting a need to improve urban First Nations' infant care in meeting the challenges of increasing urban migration.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".