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Record W2013031897 · doi:10.1080/14459795.2013.855252

An investigation of the link between gambling motives and social context of gambling in young adults

2013· article· en· W2013031897 on OpenAlexaff
Chelsea K. Quinlan, Abby L. Goldstein, Sherry H. Stewart

Bibliographic record

VenueInternational Gambling Studies · 2013
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsPsychologyGambling disorderSocial psychologyContext (archaeology)Coping (psychology)TimelineSocial environmentClinical psychologySociologyPsychiatryAddictionSocial science

Abstract

fetched live from OpenAlex

The current study examined the relationship between gambling motives and gambling in various social contexts using both retrospective and real-time assessment of gambling social context. Ninety-five young adults (79 males, 16 females; aged 19-24 years) who reported gambling at least 4 times in the past month participated. Scores on the Gambling Motives Questionnaire (GMQ; Stewart & Zack, 2008) were used as a measure of gambling motives (Enhancement, Social, Coping). Data on the social context of gambling (alone, with family, with friends, with strangers) were derived retrospectively from the Gambling Timeline Follow-Back (G-TLFB; Weinstock, Whelan, & Meyers, 2004) as well as in real time using experience sampling (ES) methods (Conner Christensen, Feldman Barrett, Bliss-Moreau, Lebo, & Kaschub, 2003). For both the G-TLFB and ES data, we conducted a series of multivariate regression analyses with the block of gambling motives predicting gambling behaviour in each social context. Across the two assessment methods, coping gambling motives positively predicted gambling alone, whereas social gambling motives negatively predicted gambling alone and positively predicted gambling with friends. These findings suggest that individuals who gamble for particular motives are more likely to do so in specific social contexts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.151
GPT teacher head0.424
Teacher spread0.273 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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