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Record W4221041733 · doi:10.1590/0102-311x00055621

Behavioral risk factors for noncommunicable diseases associated with depression and suicide risk in adolescence

2022· article· en· W4221041733 on OpenAlexaff

Bibliographic record

VenueCadernos de Saúde Pública · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversity of British Columbia
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da SaúdeCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do MaranhãoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsDepression (economics)ObesityVulnerability (computing)Suicide preventionSuicide RiskRisk factorSubstance useMental healthPoison control

Abstract

fetched live from OpenAlex

Noncommunicable diseases (NCDs) and mental disorders cooccur in adulthood, which is why their determinants and common risk factors should be addressed at an early age. Therefore, we estimated the association of the major risk factors for NCDs with depression and suicide risk by structural equation modeling considering pathways triggered by social vulnerability or mediated by obesity. This population-based study included 2,515 Brazilian adolescents. The following exposures were the major risk factors for NCDs: substance use behaviors (variable deduced from alcohol, tobacco, and drug use), physical inactivity, and components of unhealthy eating markers (added sugar and saturated fat). Obesity was assessed using the fat mass index. The outcomes were depression and suicide risk. Depression was associated with substance use behaviors (SC = 0.304; p < 0.001), added sugar (SC = 0.094; p = 0.005), and females (SC = 0.310; p < 0.001). Suicide risk was also associated with substance use behaviors (SC = 0.356; p < 0.001), added sugar (SC = 0.100; p = 0.012), and females (SC = 0.207; p < 0.001). In adolescents, these associations may help explain the cluster of NCDs and mental disorders in adulthood.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.026
GPT teacher head0.298
Teacher spread0.272 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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