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Record W2795175878 · doi:10.1093/aje/kwy062

Mortality Among Mothers Whose Children Were Taken Into Care by Child Protection Services: A Discordant Sibling Analysis

2018· article· en· W2795175878 on OpenAlexaffabout
Elizabeth Wall‐Wieler, Leslíe L. Roos, Nathan Nickel, Dan Château, Marni Brownell

Bibliographic record

VenueAmerican Journal of Epidemiology · 2018
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsManitoba HealthUniversity of Manitoba
Fundersnot available
KeywordsMedicineHazard ratioSiblingConfidence intervalChild mortalityDemographyPsychological interventionSisterPediatricsCohortMortality rateCohort studyEnvironmental healthPopulationNursingPsychologyDevelopmental psychologySurgery

Abstract

fetched live from OpenAlex

This study examines whether mothers who had a child taken into care by child protection services have higher mortality rates compared with rates seen in their biological sisters who did not have a child taken into care. We conducted this retrospective cohort study using linkable administrative data from 3,948 mothers whose oldest child was born in Manitoba, Canada, between April 1, 1992, and March 31, 2015. These mothers were from 1,974 families in which one sister had a child taken into care and one sister did not. We computed rate differences and hazard ratios of all-cause, avoidable, and unavoidable mortality. There were an additional 24 deaths per 10,000 person-years among mothers who had had a child taken into care. Mothers who had a child taken into care had higher rates of mortality due to avoidable causes (hazard ratio = 3.46; 95% confidence interval: 1.41, 8.48) and unavoidable causes (hazard ratio = 2.92; 95% confidence interval: 1.01, 8.44). The number of children taken into care did not affect mortality rates among mothers with at least 1 child taken into care. The higher mortality rates-particularly avoidable mortality-among mothers who had a child taken into care indicate a need for more specific interventions for these mothers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.029
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.359
Teacher spread0.337 · 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 teacher head, 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

Citations46
Published2018
Admission routes2
Has abstractyes

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