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Early determinants of smoking in adolescence: a prospective birth cohort study

2007· article· en· W2154119340 on OpenAlexfundno aff
Ana Maria Baptista Menezes, Pedro C. Hallal, Bernardo Lessa Horta

Bibliographic record

VenueCadernos de Saúde Pública · 2007
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da SaúdeInternational Development Research CentreWellcome TrustWorld Health Organization
KeywordsMedicineDemographyProspective cohort studyCohortCohort studyPregnancyPediatrics

Abstract

fetched live from OpenAlex

In a prospective birth cohort study in Brazil, the prevalence and early risk factors for smoking in adolescence were investigated. All 1982 hospital-born children in Pelotas, Rio Grande do Sul, Brazil, were enrolled in a birth cohort study (N = 5,914; boys: 3,037; girls: 2,877). All male participants were searched in 2000 when enrolling in the national army, and 78.8% were traced. In 2001, a systematic sample of 473 girls was interviewed, representing a follow-up rate of 69.1%. Among males, 48.6% (95%CI: 46.6-50.7) had ever tried smoking and 15.8% (95%CI: 14.3-17.3) were daily smokers. Among females, 53.1% (95%CI: 48.6-57.6) had ever tried smoking and 15.4% (95%CI: 12.1-18.7) were daily smokers. Boys born to single mothers and those with fathers with low schooling were more likely to smoke in adolescence. Girls from low-income families, with mothers who smoked during pregnancy, and fathers with alcohol-related problems were more likely to smoke. Although the smoking prevalence was similar in boys and girls, risk factors for smoking were different between the sexes. Social environment appears to be the strongest predictor of tobacco use in adolescence.

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)0.002
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.325
Teacher spread0.302 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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