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Record W2969960208 · doi:10.1080/14459795.2019.1649448

Attitudes towards gambling in a Canadian university sample of young adults

2019· article· en· W2969960208 on OpenAlexaffabout
Matthew D. Sanscartier, Jason D. Edgerton, Matthew T. Keough

Bibliographic record

VenueInternational Gambling Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of ManitobaYork UniversityCarleton University
Fundersnot available
KeywordsPsychologyYoung adultFeelingDeviance (statistics)Psychological interventionSocial norms approachSocial psychologyClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

While studies of gambling attitudes continue to grow among national adult populations and adolescents, no study to date has explored attitudes towards gambling among young adults (adults 18–25 years of age). We address this gap by exploring gambling attitudes using the Attitudes Towards Gambling Scale (ATGS) among a sample of 1,254 Canadian young adults from the University of Manitoba (n = 399 males, 32%). Results indicate that young adults are comparable to both adolescent and mature adults with respect to attitudes towards gambling, holding slightly negative feelings towards it as an activity, but feel individuals should retain the right to gamble despite personal risk. Regression analyses show that gambling, family/peer approval of gambling, and injunctive drinking norms of family and friends are the strongest predictors of favourable attitudes towards gambling. Given the strong roles of approval of gambling and drinking in young adults’ social environments, we recommend that research needs to more robustly address the normalization of multiple problem behaviours (drug use, deviance, etc.) among family and friends. We further recommend that therapeutic interventions be geared towards establishing new norms for young adults, for which group settings addressing multiple problem behaviours are especially helpful and cost-effective.

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 score0.125
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.106
GPT teacher head0.410
Teacher spread0.305 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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