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Record W3101941191 · doi:10.4309/jgi.2021.46.10

Playing to Escape: Examining Escapism in Gamblers and Gamers

2020· article· en· W3101941191 on OpenAlexvenueno aff
Erika Puiras, Shayna Cummings, Dwight Mazmanian

Bibliographic record

VenueJournal of Gambling Issues · 2020
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsEscapismPsychologyConceptualizationVideo gameSocial psychologyMultimedia

Abstract

fetched live from OpenAlex

This study examines negative and positive escapism in gamblers, gamers, and individuals who gamble and game. University students (N = 387) completed a battery of online questionnaires that included a demographic information scale, measures of the frequency and type of activity (i.e., gambling, gaming), and modified escapism scales that assessed both positive and negative escapism. Participants included 134 (34.9%) individuals who both gamble and game, 91 (23.7%) exclusive gamblers, 82 (21.4%) exclusive gamers, and 76 (19.8%) individuals who did not engage in either activity. The majority of the participants were female (74.2%). One-way analyses of variance revealed that both negative and positive escapism scores were significantly higher in gamers than in gamblers. Furthermore, individuals who both gamble and game had higher escapism scores associated with participating in gaming activities rather than gambling activities. This result suggests that individuals who play games have different motives to play than do individuals who gamble. Differences in motivation for game play may help in understanding the distinction between gamblers and gamers. As a practical implication, this distinction could be particularly relevant, given the recent blurring of boundaries between the two industries. Other practical and theoretical implications include the development of modified escapism measures for gamblers, as well as further support for the theoretical conceptualization of escapism as negative or positive.RésuméCette étude porte sur la quête d’évasion, négative ou positive, chez les adeptes des jeux de hasard, des jeux vidéo ou des deux activités à la fois. Des étudiants universitaires (N = 387) ont répondu à une batterie de questionnaires en ligne, qui comportaient une échelle de données démographiques, des mesures de la fréquence et du genre d’activité (à savoir, jeux de hasard ou jeux vidéo) ainsi que des échelles destinées à évaluer le caractère tant positif que négatif du désir d’évasion. Sur ce nombre, 134 (34,9 %) pratiquaient les deux activités; 91 (23,7 %), les jeux de hasard uniquement; 82 (21,4 %), les jeux vidéo seulement; enfin, 76 (19,8 %) ne pratiquaient ni l’une ni l’autre. Une majorité de femmes ont participé à l’étude (74,2 %). L’analyse de la variance à un facteur révèle des résultats sensiblement plus élevés, en ce qui touche les deux types d’évasion, pour les jeux vidéo par rapport aux jeux de hasard. Par ailleurs, les individus qui s’adonnent aux deux activités affichaient, dans la pratique des jeux vidéo, des résultats plus élevés que dans celle des jeux de hasard. Ce constat suggère que les motivations des adeptes de jeux vidéo diffèrent de celles des adeptes de jeux de hasard. Les différences relevées pourraient nous aider à comprendre ce qui distingue les deux types de joueurs. Compte tenu du brouillage récent des frontières entre les deux secteurs, cette observation pourrait s’avérer des plus pertinente. D’autres implications de nature pratique et théorique peuvent en découler, notamment la conception d’une échelle de mesure modifiée de la quête d’évasion s’appliquant aux adeptes des jeux de hasard, ainsi que des connaissances utiles à la conceptualisation théorique de l’évasion en tant que phénomène pouvant être négatif ou positif.

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 score0.069
Threshold uncertainty score0.732

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 head0.378
GPT teacher head0.452
Teacher spread0.074 · 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
Published2020
Admission routes1
Has abstractyes

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