MétaCan
Menu
Back to cohort
Record W2038907153 · doi:10.1037/0893-164x.18.2.170

Psychosocial variables associated with adolescent gambling.

2004· article· en· W2038907153 on OpenAlexaff
Karen K. Hardoon, Rina Gupta, Jeffrey L. Derevensky

Bibliographic record

VenuePsychology of Addictive Behaviors · 2004
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyPsychosocialClinical psychologyImpulse control disorderGambling disorderDevelopmental psychologyPsychiatryAddictionPathological

Abstract

fetched live from OpenAlex

The authors empirically examined the relations between several psychosocial variables associated with adolescent problem gambling. Participants were 2,336 students in Grades 7-13, and all completed a questionnaire regarding gambling activities, gambling severity, perceived social support, drug and alcohol dependence, and various social, emotional, and behavioral problems. With respect to gambling severity, 4.9% of adolescents met the criteria for pathological gambling, and 8.0% were found to be at risk. Psychosocial difficulties associated with problem gambling include poor perceived familial and peer social support, substance use problems, conduct problems, family problems, and parental involvement in gambling and substance use. A set of predictor variables that may lead to problem gambling includes having family problems, having conduct problems, being addicted to drugs or alcohol, and being male.

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)0.001
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.398
Teacher spread0.321 · 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

Citations280
Published2004
Admission routes1
Has abstractyes

Explore more

Same venuePsychology of Addictive BehaviorsSame topicGambling Behavior and TreatmentsFrench-language works237,207