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An extended model of gambling motives: The first results with the long and short versions of the gambling motives questionnaire-revised

2025· article· en· W4414105585 on OpenAlexafffund
Anna Mägi, Cristina Villalba-García, Borbála Paksi, Andrea Eisinger, Katalin Felvinczi, Beáta Bőthe, Gyöngyi Kökönyei, Zsolt Demetrovics, Andrea Czakó

Bibliographic record

VenueComprehensive Psychiatry · 2025
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalDalhousie University
FundersNational Research, Development and Innovation OfficeNemzeti Kutatási, Fejlesztési és Innovaciós AlapCanadian Institutes of Health ResearchFonds de Recherche du Québec-Société et CultureInnovációs és Technológiai MinisztériumNemzeti Kutatási Fejlesztési és Innovációs Hivatal
KeywordsEscapismAddictionRecreationAddictive behaviorGambling disorderRelation (database)Coping (psychology)

Abstract

fetched live from OpenAlex

Motives underlying addictions have been widely studied, using validated tools such as the Gambling Motives Questionnaire (GMQ) (Stewart and Zack, 2008). Nevertheless, subsequent studies have suggested the need to extend this model. The present paper aimed to identify potential additional factors, such as escapism, omnipotence, pleasure and financial motives, in addition to the social, enhancement and coping aspects already included. A total of 40 motivational items (adding 25 additional items to the original GMQ items) were analysed within two datasets. Sample 1 was a player panel from a gambling service provider ( N = 1829; mean age: 42.4 [SD = 13.3]; Females: 30 % [ n = 548]), while Sample 2 consisted of a nationally representative sample of the Hungarian population ( N = 437; mean age: 42.9 [SD = 13.55]; Females: 49.2 % [ N = 215]). Exploratory Factor Analysis on Sample 1 identified four factors (including a total of 27 items): coping/escapism, social motives, enhancement/pleasure, and financial motives. The four-factor structure was confirmed on Sample 2 with confirmatory factor analysis showing adequate model fit (CFI = 0.987; TLI = 0.986; RMSEA[CI] = 0.047 [0.041–0.052]); however, high inter-factor correlations were evident in the general population sample. A shorter, 14-item version of the scale was also suggested. Although the newly identified motives overlap with the original ones, the content of the factors enables the inclusion of certain aspects, like escapism within the coping factor, that proved to be the most important in relation to other potentially addictive behaviours. This suggests that examining the role of motives in gambling may be crucial in differentiating between problem gambling and recreational gambling. • Examining the role of motives in addictions is crucial in understanding and preventing them. • Coping, social, enhancement or financial motives predict gambling severity. • Escapism motive might be pivotal in the development of addictive behaviours. • Social motives for gambling might play a protective role. • GMQ-R-27 is a revised and valid measure of gambling motives, including new aspects like escapism.

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 score0.056
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.069
GPT teacher head0.369
Teacher spread0.300 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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