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Record W2791547680 · doi:10.1111/dar.12696

Do public expenditures on health and families relate to alcohol abstaining in adolescents? Multilevel study of adolescents in 24 countries

2018· article· en· W2791547680 on OpenAlexaff
Alessio Vieno, Gianmarco Altoè, Emmanuel Kuntsche, Frank J. Elgar

Bibliographic record

VenueDrug and Alcohol Review · 2018
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcGill University Health Centre
FundersUniversity of St Andrews
KeywordsAbstinenceMultilevel modelPublic healthGovernment (linguistics)PsychologyEnvironmental healthAdolescent healthMedicineDemographyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Several European countries have observed an increase in the proportion of adolescents that abstain from drinking alcohol in the last decade. The reasons for this trend remain underexplored. We hypothesised that more generous government expenditures on health services and benefits to families with children relate to a positive trend in abstainers. DESIGN AND METHODS: We used data on 15-year-olds in four successive cycles of the World Health Organization Health Behaviour in School-aged Children study (2002 to 2014) in 24 North American and European countries (pooled n = 175 331). Generalised linear mixed-effects models were tested to analyse trends in alcohol abstinence and to investigate whether cross-country differences in these trends relate to public expenditures on health and families with children (in proportion to gross domestic product). RESULTS: Overall, we observed an increase in the proportion of abstainers from 21% in 2002 to 35% in 2014. An exception was Greece where abstaining had decreased from 20% to 15%. Similar results were found in boys and girls. The upward trend in abstinence related to larger government expenditures on health and families. DISCUSSIONS AND CONCLUSIONS: More generous expenditures on health services and family benefits relate to more adolescents abstaining from alcohol.

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 score0.087
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.068
GPT teacher head0.366
Teacher spread0.298 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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