Ehawawisit: Sociodemographic and Clinical Characteristics and Perinatal Outcomes of Métis Pregnancies in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: This study aimed to evaluate sociodemographic and clinical characteristics and perinatal outcomes of Métis pregnancies in Alberta, Canada. METHODS: weeks gestation) in Alberta (2006-2016). Métis births were identified through linkage of the Otipemisiwak Métis Government of the Métis Nation within Alberta Identification Registry with Alberta's population and perinatal registries, and compared with non-Métis pregnancies. Age-standardized prevalence and adjusted odds ratios (aOR) with 95% CIs were calculated. RESULTS: The study included 7910 Métis and 471 522 non-Métis pregnancies. Métis pregnancies had higher rates of self-reported smoking (aOR 5.83; 95% CI 4.99-6.80), substance use during pregnancy (aOR 1.92; 95% CI 1.65-2.23), and pre-existing chronic hypertension (aOR 1.57; 95% CI 1.11-2.25). Métis deliveries had more obstetric hemorrhage complications (aOR 1.11; 95% CI 1.01-1.23), higher odds of spontaneous vaginal delivery (aOR 1.25; 95% CI 1.11-1.40), and lower odds of vacuum- or forceps-assisted delivery (aOR 0.69; 95% CI 0.63-0.75). Métis newborns had higher birth weights (3.42 kg vs. 3.35 kg), were more often large for gestational age (aOR 1.50; 95% CI 1.35-1.66), and less often small for gestational age (aOR 0.70; 95% CI 0.62-0.79). Rates of preterm births, neonatal intensive care unit admissions, and neonatal deaths were similar to those of non-Métis newborns. CONCLUSIONS: Métis pregnancies had low overall perinatal complication rates but distinct differences in maternal and perinatal outcomes relative to non-Métis pregnancies. Métis-specific, culturally sensitive care may support improved perinatal outcomes for Métis families.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".