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Record W4400320834 · doi:10.23889/ijpds.v9i2.2396

Maternal disability and newborn discharge to social services: a population-based study

2024· article· en· W4400320834 on OpenAlexafffundabout
Claire Grant, Yona Lunsky, Astrid Guttmann, Simone N. Vigod, Isobel Sharpe, Kinwah Fung, Hilary K. Brown

Bibliographic record

VenueInternational Journal for Population Data Science · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsPublic Health OntarioThe Scarborough HospitalHospital for Sick ChildrenWomen's College HospitalUniversity of TorontoSickKids FoundationCentre for Addiction and Mental Health
FundersEconomic and Social Research CouncilCanadian Institutes of Health ResearchMitacsCanada Research ChairsUK Research and Innovation
KeywordsPopulationPsychologyMedicineDevelopmental psychologyEnvironmental health

Abstract

fetched live from OpenAlex

Introduction: Removing a child from their family is the option of last resort for social services. However, decisions to place children into care are occurring more frequently and earlier in children's lives, with newborn discharge to social services being a particular concern due to the effects of mother-newborn separations on child development. Women with disabilities face negative assumptions about their parenting capacity, but little is known about their rates of newborn discharge to social services. Objectives: To examine the risk of discharge to social services among newborns of women with and without disabilities. Methods: We conducted a population-based cohort study of singleton livebirths in Ontario, Canada, 2008-2019. We used modified Poisson regression to estimate the relative risk (RR) of discharge to social services immediately after the birth hospital stay, comparing newborns of women with physical (n = 114,685), sensory (n = 38,268), intellectual/developmental (n = 2,094), and multiple disabilities (n = 8,075) to newborns of women without a disability (n = 1,221,765). Within each group, we also examined maternal sociodemographic, health, health care, and pregnancy-related characteristics associated with the outcome. Results: Compared to newborns of women without disabilities (0.2%), newborns of women with physical (0.5%; aRR 1.53, 95% CI 1.39-1.69), sensory (0.4%; aRR 1.34, 95% CI 1.12-1.59), intellectual/developmental (5.6%; aRR 5.34, 95% CI 4.36-6.53), and multiple disabilities (1.7%; aRR 3.09, 95% CI 2.56-3.72) had increased risk of being discharged to social services after the birth hospital stay. Within each group, the strongest predictors of the outcome were young maternal age, low income quintile, social assistance, maternal mental illness and substance use disorders, inadequate prenatal care, and neonatal morbidity. Conclusions: Newborns of women with disabilities are at increased risk of being discharged to social services after the birth hospital stay. These findings can be used to inform the development of tailored supports for new mothers with disabilities and their infants.

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)0.001
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 score0.651
Threshold uncertainty score0.702

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.430
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".

Quick stats

Citations2
Published2024
Admission routes3
Has abstractyes

Explore more

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