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Record W4408836718 · doi:10.3389/ijph.2025.1608136

Four Decades of Advancing Research on Adolescent Health and Informing Health Policies: The Health Behaviour in School-Aged Children Study

2025· article· en· W4408836718 on OpenAlexaff
Oddrun Samdal, Colette Kelly, Wendy Craig, John Hancock, Bente Wold, Leif Edvard Aarø, Jo Inchley

Bibliographic record

VenueInternational Journal of Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsQueen's University
FundersUniversitetet i Bergen
KeywordsPublic healthAdolescent healthHealth promotionGeneral partnershipHealth policyEnvironmental healthPsychologyMedicinePolitical scienceEconomic growthNursing

Abstract

fetched live from OpenAlex

The Health Behaviour in School-aged Children (HBSC) study is a large cross-national research study, conducted in partnership with the World Health Organization (WHO). The study has surveyed young people aged 11, 13 and 15 years every 4 years since the mid-1980s and has grown to include 50 countries across Europe, North America, and Western-Central Asia. Over the past 40 years more than 1.6 million students have participated. HBSC aims to advance understanding of adolescent health behaviours, health and wellbeing within social contexts, inform national and international health promotion policies and practice, and foster collaboration among researchers, policymakers, and practitioners. In this paper we share the history and development of the HBSC study covering: i) theory-driven and novel research impact, ii) unique long-term trends in adolescent health behaviours and perceived health and wellbeing, iii) methodological rigor to allow cross-national comparison, and iv) embedding youth involvement and maximizing policy impact.

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)0.039
metaresearch head score (Gemma)0.048
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.048
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.011
Science and technology studies0.0030.004
Scholarly communication0.0070.003
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.213
GPT teacher head0.587
Teacher spread0.375 · 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

Citations5
Published2025
Admission routes1
Has abstractyes

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