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Record W2557068866 · doi:10.1111/ina.12353

Prospective longitudinal associations between household smoke exposure in early childhood and antisocial behavior at age 12

2016· article· en· W2557068866 on OpenAlexaffabout
Linda S. Pagani, F. Lévesque-Seck, Isabelle Archambault, Michel Janosz

Bibliographic record

VenueIndoor Air · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsLongitudinal studyAggressionStandard deviationConfidence intervalProspective cohort studyDemographyPsychologyUnit (ring theory)MedicineConfoundingPediatricsDevelopmental psychologyEnvironmental healthStatistics

Abstract

fetched live from OpenAlex

Young children exert little control over household tobacco smoke exposure, which is considered a developmental neurotoxicant. Using the Quebec Longitudinal Study birth cohort, we examine prospective associations between early childhood smoke exposure and later antisocial behavior. Parents of 1035 children reported on the presence of household smokers at seven follow-ups from ages 1.5 to 7.5. At age 12, children self-reported on five aspects of early antisocial dispositions. After adjusting for confounders, every standard deviation increase in household smoke exposure was prospectively associated with a 19% standard deviation unit increase in conduct problems (β=0.07; 95% confidence interval [CI] from 0.04 to 0.09), a 11% standard deviation unit increase in proactive aggression (β=0.04; 95% CI from 0.01 to 0.07), a 13% standard deviation unit increase in reactive aggression (β=0.07; 95% CI from 0.03 to 0.12), a 14% standard deviation unit increase in school indiscipline (β=0.13; 95% CI from 0.05 to 0.20), and a 10% standard deviation unit increase in dropout risk (β=0.07; 95% CI from 0.01 to 0.12). These long-term findings warrant fostering parental awareness of developmental risks by policy-makers/health practitioners. School curricula can equally integrate these ideas into their curriculum.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.546

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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

Citations16
Published2016
Admission routes2
Has abstractyes

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