Supporting health and well-being among infants born to First Nations parents experiencing incarceration: a partnership-based whole-population administrative data study.
Bibliographic record
Abstract
ObjectivesGenerations of racist and colonial policies have resulted in First Nations (FN) people being systematically over-represented in Canada’s legal system. FN researchers partnered with data scientists at the University of Manitoba to document the birth outcomes associated with experiences of prenatal incarceration among FN families. ApproachThis retrospective cohort study linked whole-population administrative data from (i) Manitoba’s legal system to identify infants born to people incarcerated while pregnant, (ii) the First Nations research file to identify FN families, (iii) hospital records for birth outcomes, (iii) health and social services data for measured confounders. All Manitoba residents with a live birth (Jan 2004 - Dec 2017), and their infants, were eligible. Generalized linear models tested for differences in birth outcomes associated with experiencing incarceration while pregnant. Propensity score weights adjusted for measured confounders. Effect modification analyses tested whether associations differed between FN and all other Manitobans (AOM). ResultsFN people were more likely to experience incarceration while pregnant (n=1449) than AOM (n=278). Before propensity score adjustment, incarcerated pregnant people differed on important sociodemographic confounding characteristics from pregnant people who were not incarcerated – e.g., lower socioeconomic status, higher prevalence of pre-existing mental disorders, higher prevalence of having a previous child taken into care of family services, more likely to live in an urban setting. After propensity score adjustment, confounding characteristics were balanced between exposure groups. After adjustment, infants born to people incarcerated while pregnant were more likely to be low birth weight at term (aRR 1.76; 95% CI 1.41-2.18), be born preterm (aRR 1.44; 1.33-1.56), be small for gestational age (aRR 1.40; 1.28-1.54). Associations did not differ between FN and AOM families. ConclusionIncarceration of pregnant people compromises their infant’s birth outcomes and perpetuates intergenerational systems of oppression that exacerbate health inequities. To improve the health and well-being of FN people, we must implement Calls to Action outlined by the Truth and Reconciliation Commission to redress these harms experienced by FN people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".