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Record W2516480176 · doi:10.4309/jgi.2016.33.3

Parental and peer influences on emerging adult problem gambling: Does exposure to problem gambling reduce stigmatizing perceptions and increase vulnerability?

2016· article· en· W2516480176 on OpenAlexvenueno aff
Jessica Gay, Peter Gill, Denise Corboy

Bibliographic record

VenueJournal of Gambling Issues · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyVulnerability (computing)PerceptionDevelopmental psychologyStigma (botany)Young adultClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Research has identified 18 to 30 years olds as the biggest spenders on gambling activities, with significantly higher prevalence of gambling problems than other age groups. Identifying the factors that influence the development of gambling problems in young people is important for guiding prevention strategies. This study aimed to analyse how emerging adult problem gambling is influenced by the people around them. In particular, we explored whether perceived parental and peer problem gambling predicted emerging adult problem gambling, and whether reduced gambling self-stigma mediated these relationships. A community sample of 188 Australian gamblers aged 18 to 29 (M = 21.41, SD = 2.99) completed three versions of the Problem Gambling Severity Index (PGSI) and the Gambling Perception Scale. Results indicated that perceived parental and peer gambling were positively related to emerging adult problem gambling. While reduced gambling helping stigma was related to higher problem gambling, stigma did not mediate the links between significant others' gambling and emerging adult problem gambling. We conclude that social influences are important in the development of problem gambling for young people, and that older male emerging adults who have a gambling mother are at most risk of problem gambling.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.110
GPT teacher head0.424
Teacher spread0.314 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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