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Record W2113821956 · doi:10.1093/eurpub/ckv031

Decreases in adolescent weekly alcohol use in Europe and North America: evidence from 28 countries from 2002 to 2010

2015· article· en· W2113821956 on OpenAlexaff
Margaretha de Looze, Quinten A. W. Raaijmakers, Tom ter Bogt, Pernille Bendtsen, T. Farhat, M. Ferreira, Emmanuelle Godeau, Emmanuel Kuntsche, Michal Molcho, Timo‐Kolja Pförtner, Bruce G. Simons‐Morton, Alessio Vieno, Wilma Vollebergh, William Pickett

Bibliographic record

VenueEuropean Journal of Public Health · 2015
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsQueen's University
FundersUniversitetet i BergenUniversity of St Andrews
KeywordsDemographyMedicineEnvironmental healthGeographySociology

Abstract

fetched live from OpenAlex

BACKGROUND: This study examined trends in adolescent weekly alcohol use between 2002 and 2010 in 28 European and North American countries. METHODS: Analyses were based on data from 11-, 13- and 15-year-old adolescents who participated in the Health Behaviour in School-Aged Children (HBSC) study in 2002, 2006 and 2010. RESULTS: Weekly alcohol use declined in 20 of 28 countries and in all geographic regions, from 12.1 to 6.1% in Anglo-Saxon countries, 11.4 to 7.8% in Western Europe, 9.3 to 4.1% in Northern Europe and 16.3 to 9.9% in Southern Europe. Even in Eastern Europe, where a stable trend was observed between 2002 and 2006, weekly alcohol use declined between 2006 and 2010 from 12.3 to 10.1%. The decline was evident in all gender and age subgroups. CONCLUSIONS: These consistent trends may be attributable to increased awareness of the harmful effects of alcohol for adolescent development and the implementation of associated prevention efforts, or changes in social norms and conditions. Although the declining trend was remarkably similar across countries, prevalence rates still differed considerably across countries.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.219
GPT teacher head0.337
Teacher spread0.118 · 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

Citations209
Published2015
Admission routes1
Has abstractyes

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Same venueEuropean Journal of Public HealthSame topicSubstance Abuse Treatment and OutcomesFrench-language works237,207