An unconditional prenatal income supplement is associated with improved birth and early childhood outcomes among First Nations children in Manitoba, Canada: a population-based cohort study
Bibliographic record
Abstract
Abstract Background In Manitoba, Canada, low-income pregnant women are eligible for the Healthy Baby Prenatal Benefit, an unconditional income supplement of up to CAD $81/month, during their latter two trimesters. Our objective was to determine the impact of the Healthy Baby Prenatal Benefit on birth and early childhood outcomes among Manitoba First Nations women and their children. Methods We used administrative data to identify low-income First Nations women who gave birth 2003–2011 (n = 8209), adjusting for differences between women who received (n = 6103) and did not receive the Healthy Baby Prenatal Benefit (n = 2106) with using propensity score weighting. Using multi-variable regressions, we compared rates of low birth weight, preterm, and small- and large-for-gestational-age births, 5-min Apgar scores, breastfeeding initiation, birth hospitalization length of stay, hospital readmissions, complete vaccination at age one and two, and developmental vulnerability in Kindergarten. Results Women who received the benefit had lower risk of low birth weight (adjusted relative risk [aRR] 0.74; 95% CI 0.62–0.88) and preterm (aRR 0.77; 0.68–0.88) births, and were more likely to initiate breastfeeding (aRR 1.05; 1.01–1.09). Receipt of the Healthy Baby Prenatal Benefit was also associated with higher rates of child vaccination at age one (aRR 1.10; 1.06–1.14) and two (aRR 1.19; 1.13–1.25), and a lower risk that children would be vulnerable in the developmental domains of language and cognitive development (aRR 0.88; 0.79–0.98) and general knowledge/communication skills (aRR 0.87; 0.77–0.98) in Kindergarten. Conclusions A modest unconditional income supplement of CAD $81/month during pregnancy was associated with improved birth outcomes, increased vaccination rates, and better developmental health outcomes for First Nations children from low-income 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".