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Does Household Structure Affect Adolescent Smoking?

2011· article· en· W2162464231 on OpenAlexaffabout
Neda Razaz‐Rahmati, Sayed Reza Nourian, Chizimuzo T.C. Okoli

Bibliographic record

VenuePublic Health Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOddsContext (archaeology)Logistic regressionOdds ratioAffect (linguistics)Youth smokingDemographyMultivariate analysisPublic healthPsychologyEnvironmental healthMedicineGerontologyGeographyTobacco control

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine household structure when studying determinants of youth smoking, as the configuration of a family is an important factor in the etiology of adolescent problem behaviors. DESIGN AND SAMPLE: The study sample (n = 13,001) included respondents aged 12-19 years who were either living in two-parent households, single-parent households, or no-parent households, and with valid response to the smoking status questions from the Canadian Community Health Survey. MEASURES: Multivariate logistic regression was used to test the presence and strength of the association between household structure and the likelihood of smoking while controlling for age, sex, household education, and exposure to secondhand smoking. RESULTS: The odds of youth smoking in the single-parent household was 1.78 times greater than the odds of youth smoking in two-parent households. Similarly, the odds of youth smoking in no-parent households was 1.47 times greater than the odds of youth smoking in two-parent households. CONCLUSIONS: The results indicate that there is an association between household structure and smoking among adolescents in Canada. Findings might be helpful for decision makers to recognize the context within which adolescents initiate and sustain smoking when developing strategies for the prevention and cessation of smoking among youth.

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 score0.243
Threshold uncertainty score0.415

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)0.0000.000

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 head0.113
GPT teacher head0.345
Teacher spread0.232 · 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

Citations18
Published2011
Admission routes2
Has abstractyes

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