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Record W2955512409 · doi:10.1037/adb0000482

Longitudinal interplay between gambling participation and substance use during late adolescence: A genetically-informed study.

2019· article· en· W2955512409 on OpenAlexfundno aff
Frank Vitaro, Daniel J. Dickson, Mara Brendgen, Éric Lacourse, Ginette Dionne, Michel Boivin

Bibliographic record

VenuePsychology of Addictive Behaviors · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchFonds de recherche du Québec
KeywordsPsychologySubstance useDevelopmental psychologySubstance abuseClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Substance use and gambling participation during adolescence are correlated, both concurrently and over time. It is unclear, however, whether this association can be explained by common underlying genetic vulnerabilities or environmental factors. The present study explored the concurrent and longitudinal associations between substance use and gambling participation and their genetic and environmental underpinnings by late adolescence. Participants were 373 pairs of monozygotic and dizygotic twins. Self-reports of substance use and gambling participation were collected at Ages 17 and 19 years. Results showed concurrent associations between substance use and gambling participation as well as a small, but significant unidirectional longitudinal association over time from substance use to gambling participation. Common genetic factors largely accounted for the concurrent associations at Ages 17 and 19, as well as for the unidirectional longitudinal association between substance use and gambling participation. Substance use and gambling participation share a common genetic component that account for most of their concurrent and longitudinal links during late adolescence. However, these behaviors are also influenced by specific environmental factors. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.111
GPT teacher head0.450
Teacher spread0.339 · 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.

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

Citations9
Published2019
Admission routes1
Has abstractyes

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