Birth outcomes among Métis women and infants in Manitoba, Canada: A linked administrative data study using health system and justice system data.
Bibliographic record
Abstract
ObjectiveManitoba has one of the highest incarceration rates among Canadian provinces, and because of Canada’s racist and colonial policies, the number of Métis women in custody is disproportionately high. In this study, we examined birth outcomes among Métis and all other Manitoba women who were incarcerated while pregnant. ApproachUsing linked administrative health and justice system data, we developed a 2004-2017 cohort of mother-infant dyads with a live birth (n= mothers; n= infants). We identified all Métis mother-infant dyads, and then divided them into 3 mutually exclusive groups: a) prenatal incarceration; b) post-natal incarceration; c) no incarceration. We developed matched comparison groups for each exposure based on Métis identity, maternal age, geography and income, and, after adjusting for other potentially confounding variables (e.g., maternal age, maternal mental health, and socioeconomic factors), we examined risk differences in birth outcomes among Métis women and infants between exposure groups. ResultsThe cohort included n=534 mothers who were incarcerated while pregnant (a), n=1,677 mothers incarcerated postnatally (b), and n=14,084 mothers never incarcerated (c). When we compared prenatal vs postnatal incarceration (a vs b), we found that women incarcerated during the prenatal period were more likely to have a low birth weight infant (RD: 4.2, 95% CI: 3.0, 5.4), preterm birth (RD: 5.1, 95% CI: 3.7, 6.5) or Caesarean birth (RD: 5.5, 95% CI: 3.8, 7.1) and were less likely to have a large-for-gestational-age infant (RD: -3.8, 95% CI: -5.1, -2.5) or to initiate breast feeding (-3.4, 95% CI: -5.5, -1.3). In the prenatal vs no incarceration comparison (a vs c), the same outcomes were statistically significant as above; however, the risk differences were even more pronounced. ConclusionPrenatal incarceration is associated with poor birth outcomes for all mothers and infants in Manitoba; however, Métis women and children are disproportionately affected, further perpetuating inequities they may already experience. Canadians must acknowledge the harms of racist policies and practices, and work to support the health and well-being of Métis 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".