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Record W2955579055 · doi:10.1177/0272431619858413

National Child and Adolescent Health Policies as Indicators of Adolescent Mental Health: A Multilevel Analysis of 30 European Countries

2019· article· en· W2955579055 on OpenAlexaff
Anne M. Hendriks, Meike Bartels, Gonneke W. J. M. Stevens, Sophie D. Walsh, Torbjørn Torsheim, Frank J. Elgar, Catrin Finkenauer

Bibliographic record

VenueThe Journal of Early Adolescence · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMcGill University
FundersSeventh Framework ProgrammeEuropean CommissionWorld Health Organization
KeywordsMental healthAdolescent healthOccupational safety and healthLife satisfactionPoison controlSuicide preventionMultilevel modelHuman factors and ergonomicsPsychologyInjury preventionMedicinePsychiatryEnvironmental healthSocial psychology

Abstract

fetched live from OpenAlex

There is little evidence on the association between child and adolescent mental health (CAMH) policies and adolescent mental health. This study examined this association using data on indicators of adolescent mental health—aggressive behavior, life satisfaction, and psychosomatic symptoms—in 172,829 eleven- to fifteen-year-olds from 30 European countries in the 2013-2014 Health Behaviour in School-Aged Children (HBSC) study. Individual records were linked to national-level policies for CAMH, controlling for national-level adult violence, adult well-being, and income inequality. Multilevel analyses revealed lower adolescent aggressive behavior in countries with more CAMH policies, even after controlling for other national-level indicators. Adolescent life satisfaction and psychosomatic symptoms were not associated with CAMH policies. Results may inform policy recommendations regarding investments in adolescent mental health.

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)0.004
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 score0.067
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.337
Teacher spread0.315 · 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

Citations22
Published2019
Admission routes1
Has abstractyes

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