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Record W2945095225 · doi:10.1542/peds.2018-3426

Poverty and Early Childhood Outcomes

2019· article· en· W2945095225 on OpenAlexaffabout
Leslíe L. Roos, Elizabeth Wall‐Wieler, Janelle Boram Lee

Bibliographic record

VenuePEDIATRICS · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPovertyOddsMedicineOdds ratioChild povertyWelfareDemographyLogistic regressionEconomic growthEconomicsSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Children born into poverty face many challenges. Exposure to poverty comes in different forms, and children may also transition into or out of poverty. In this study, we examine the relationships among various outcomes and different levels of poverty (household and/or neighborhood poverty) at different points during a child’s first 5 years. METHODS: We used linkable administrative databases, following 46 589 children born in Manitoba, Canada, between 2000 and 2009 to age 7. Poverty is defined as those receiving welfare and those living in low-income neighborhoods. Four outcomes are measured in the first 5 years (placement in out-of-home care, externalizing mental health diagnosis, asthma diagnosis, and hospitalization for injury), with school readiness assessed between ages 5 and 7. RESULTS: Children born into poverty had greater odds of not being ready for school than children not born into poverty (adjusted odds ratio = 1.54, 1.59, 1.26 for children born in household and neighborhood poverty, household poverty only, and neighborhood poverty only, respectively; all significant at P < .05). Similar patterns were seen across outcomes. For those born into neighborhood poverty, the odds of school readiness were higher only if children moved before age 2. CONCLUSIONS: The level of poverty (household or neighborhood) and its duration modify the relationship between early poverty and childhood outcomes. Covariate adjustment generally weakens but does not eliminate these relationships.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.245
Teacher spread0.237 · 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.

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

Citations76
Published2019
Admission routes2
Has abstractyes

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