No Strings Attached: The Impact of an Unconditional Prenatal Income Supplement on First Nations Birth and Early Childhood Outcomes
Bibliographic record
Abstract
IntroductionIn Manitoba, low-income pregnant women are eligible for the Healthy Baby Prenatal Benefit (HBPB), an unconditional income supplement provided during the second and third trimester of pregnancy. HBPB is associated with improved birth outcomes for Manitoba women; its association with birth outcomes for First Nations (Indigenous) women is unknown. Objectives and ApproachTo determine the association between HBPB and First Nations’ (FN) newborn and early childhood outcomes, we linked whole-population data from health, public health, family services and education. We included only FN women receiving income assistance during pregnancy (n=7074) to develop comparable treatment (received HBPB; n=5283) and comparison (no HBPB; n=1791) groups. Propensity score weighting adjusted for differences in maternal characteristics between groups. Multi-variable regressions compared groups on breastfeeding initiation, low birth weight, preterm birth, small- and large-for-gestational age, Apgar scores, complete immunizations at 1 and 2 years, and developmental vulnerability in kindergarten measured with the Early Development Instrument (EDI). ResultsReceipt of the HBPB was associated with reductions in low birth weight births (adjusted Relative Risk (aRR): 0.77; 95% CI: 0.63, 0.93) and preterm births (aRR: 0.78 (0.68, 0.90)), and increases in breastfeeding initiation (aRR: 1.05 (1.00, 1.09)) and large-for-gestational age births (aRR: 1.11 (1.01, 1.23)). HBPB receipt during pregnancy was also associated with increases in 1- and 2-year immunizations for FN children (aRR: 1.14 (1.09, 1.19), and aRR: 1.28 (1.19, 1.36), respectively). Reductions in the risk of being developmentally vulnerable in the language and cognitive domain of the EDI were also found for FN children whose mothers had received the HBPB during pregnancy (aRR: 0.85 (0.74, 0.97). Conclusion/ImplicationsA modest unconditional income supplement during pregnancy was associated with improved birth outcomes, increased immunization rates, and improved language and cognitive development at kindergarten for children born to low-income First Nations women. Long-term strategies to address structural inequities and the ongoing effects of colonization are also needed.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.002 |
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".