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Record W4294243074 · doi:10.23889/ijpds.v7i3.1948

Supporting health and well-being among infants born to First Nations parents experiencing incarceration: a partnership-based whole-population administrative data study.

2022· article· en· W4294243074 on OpenAlexaffabout
Nathan Nickel, Wanda Phillips-Beck

Bibliographic record

VenueInternational Journal for Population Data Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of ManitobaFirst Nations Health and Social Secretariat of ManitobaManitoba Health
Fundersnot available
KeywordsPropensity score matchingConfoundingMedicineSocioeconomic statusDemographyPopulationCohort studyLow birth weightBirth weightPregnancyEnvironmental health

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.096
GPT teacher head0.470
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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