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Record W1700723963 · doi:10.1186/1471-2458-6-280

Smoking trends among adolescents from 1990 to 2002 in ten European countries and Canada

2006· article· en· W1700723963 on OpenAlexaboutno aff
Anne Hublet, Dirk De Bacquer, Raili Välimaa, Emmanuelle Godeau, Holger Schmid, Giora Rahav, Lea Maes

Bibliographic record

VenueBMC Public Health · 2006
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersVlaamse regeringUniversitetet i Bergen
KeywordsBiostatisticsMedicineDemographyPublic healthSmoking prevalenceEpidemiologyLogistic regressionEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Daily smoking adolescents are a public health problem as they are more likely to become adult smokers and to develop smoking-related health problems later on in their lives. METHODS: The study is part of the four-yearly, cross-national Health Behaviour in School-aged Children study, a school-based survey on a nationally representative sample using a standardised methodology. Data of 4 survey periods are available (1990-2002). Gender-specific daily smoking trends among 14-15 year olds are examined using logistic regressions. Sex ratios are calculated for each survey period and country. Interaction effects between period and gender are examined. RESULTS: Daily smoking prevalence in boys in 2002 ranges from 5.5% in Sweden to 20.0% in Latvia. Among girls, the daily smoking prevalence in 2002 ranges from 8.9% in Poland to 24.7% in Austria. Three daily smoking trend groups are identified: countries with a declining or stagnating trend, countries with an increasing trend followed by a decreasing trend, and countries with an increasing trend. These trend groups show a geographical pattern, but are not linked to smoking prevalence. Over the 4 surveys, the sex ratio has changed in Belgium, Switzerland, and Latvia. CONCLUSION: Among adolescents in Europe, three groups of countries in a different stage of the smoking epidemic curve can be identified, with girls being in an earlier stage than boys. In 2002, large differences in smoking prevalence between the countries have been observed. This predicts a high mortality due to smoking over 20-30 years for some countries, if no policy interventions are taken.

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)0.001
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 score0.014
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.027
GPT teacher head0.279
Teacher spread0.251 · 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

Citations108
Published2006
Admission routes1
Has abstractyes

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