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Record W3139165204 · doi:10.1037/adb0000716

Gambling-related psychological predictors and moderators of attentional bias among electronic gaming machine players.

2021· article· en· W3139165204 on OpenAlexafffund
Hyoun S. Kim, Christopher R. Sears, David C. Hodgins, Emma V. Ritchie, Kristy R. Kowatch, Daniel S. McGrath

Bibliographic record

VenuePsychology of Addictive Behaviors · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsLakehead UniversityYork UniversityUniversity of CalgaryToronto Metropolitan University
FundersAlberta Gambling Research Institute, University of Calgary
KeywordsPsychologyGambling disorderContext (archaeology)Attentional biasCravingClinical psychologyAddictionPsycINFOCognitionSalience (neuroscience)Alcohol use disorderPsychiatryAlcoholCognitive psychologyMEDLINE

Abstract

fetched live from OpenAlex

The results provide further evidence that ABs develop in the context of excessive gambling and are associated with gambling and psychological variables. The findings support the incentive-salience theory of ABs in gambling and provide a rationale for the development of AB modification programs in the treatment of gambling disorder. Given the predominantly white sample, our results may not generalize to individuals of other ethnicities. (PsycInfo Database Record (c) 2021 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)0.001
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 score0.005
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.373
Teacher spread0.336 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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Same venuePsychology of Addictive BehaviorsSame topicImpact of Technology on AdolescentsFrench-language works237,207