Neighborhood Socioeconomic Characteristics, Birth Outcomes and Infant MortalityamongFirstNations and Non-First Nations in Manitoba, Canada~!2009-11-30~!2010-04-12~!2010-07-06~!
Bibliographic record
Abstract
OBJECTIVE: Little is known about the possible impacts of neighborhood socioeconomic status on birth outcomes and infant mortality among Aboriginal populations. We assessed birth outcomes and infant mortality by neighborhood socioeconomic status among First Nations and non-First Nations in Manitoba. STUDY DESIGN: We conducted a retrospective birth cohort study of all live births (26,176 First Nations, 129,623 non-First Nations) to Manitoba residents, 1991-2000. Maternal residential postal codes were used to assign four measures of neighborhood socioeconomic status (concerning income, education, unemployment, and lone parenthood) obtained from 1996 census data. RESULTS: First Nations women were much more likely to live in neighborhoods of low socioeconomic status. First Nations infants were much more likely to die during their first year of life [risk ratio (RR) =1.9] especially during the postneonatal period (RR=3.6). For both First Nations and non-First Nations, living in neighborhoods of low socioeconomic status was associated with an increased risk of infant death, especially postneonatal death. For non-First Nations, higher rates of pre-term and small-for-gestational-age birth were consistently observed in low socioeconomic status neighborhoods, but for First Nations the associations were less consistent across the four measures of socioeconomic status. Adjusting for neighborhood socioeconomic status, the disparities in infant and postneonatal mortality between First Nations and non-First Nations were attenuated. CONCLUSION: Low neighborhood socioeconomic status was associated with an elevated risk of infant death even among First Nations, and may partly account for their higher rates of infant mortality compared to non-First Nations in Manitoba.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".