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Record W2019666255 · doi:10.1155/2010/795265

Use of Alternative Tobacco Products in Multiethnic Youth from Jujuy, Argentina

2010· article· en· W2019666255 on OpenAlexfundno aff
Ethel Alderete, Celia P. Kaplan, Steven E. Gregorich, Eliseo J. Pérez‐Stable

Bibliographic record

VenueJournal of Environmental and Public Health · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Institutes of HealthNational Cancer InstituteUniversity of California, San FranciscoNational Institute on Drug AbuseFogarty International CenterInternational Development Research Centre
KeywordsEnvironmental healthTobacco useTraditional medicineMedicineGeographyPopulation

Abstract

fetched live from OpenAlex

This study examines alternative tobacco use among Latin American youth. A self-administered survey in a random sample of 27 schools was administered in 2004 in Jujuy, Argentina (N = 3218). Prevalence of alternative tobacco product use was 24.1%; 15.3% of youth used hand-rolled cigarettes, 7.8% smoked cigars, 2.3% chewed tobacco leaf and 1.6% smoked pipe. Among youth who never smoked manufactured cigarettes, alternative product use was rare (2.9%), except for chewing tobacco (22%). In multivariate logistic regression boys were more likely than girls to smoke pipe (OR = 3.1; 95% CI 1.1-8.7); indigenous language was associated with smoking hand-rolled cigarettes (OR = 1.4; 95% CI-1.1-1.9) and pipe (OR = 2.2; 95% CI 1.5-3.4). Working in tobacco sales was a risk factor for chewing tobacco (OR = 2.9; 95% CI: 1.7-4.9) and smoking hand-rolled cigarettes (OR = 1.4; 95% CI 1.1-1.8). Having friends who smoked was associated with chewing tobacco (OR = 1.8; 95% CI 1.0-3.2) and with smoking cigars (OR = 2.1; 95% CI 1.5-2.9). Current drinking and thrill-seeking orientation were associated with cigars and pipe smoking. Findings highlight the importance of surveillance of alternative tobacco products use and availability among youth and for addressing identified risk factors.

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 score0.033
Threshold uncertainty score0.235

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 head0.090
GPT teacher head0.304
Teacher spread0.214 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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