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Record W3003273535 · doi:10.4309/jgi.2019.43.4

Electronic Gambling Machines Outside Casinos: An Environmental Study of Risk Factors in Gambling Venues

2019· article· en· W3003273535 on OpenAlexaffvenueabout
Maxime Chrétien, Annie Goulet, Daniel Fortin‐Guichard, Joanne Castonguay, Sophie Derguy, Stéphanie Hamel, Christian Jacques, Dominic Nadeau, Isabelle Giroux

Bibliographic record

VenueJournal of Gambling Issues · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanUniversité Laval
Fundersnot available
KeywordsLicenseRevenueAdvertisingHumanitiesBusinessPolitical scienceArtFinanceLaw

Abstract

fetched live from OpenAlex

Electronic gambling machine (EGM) licenses are meant to be a complementary revenue source for liquor establishments. Considering this, retailers with more than one license to operate EGMs may benefit from promoting their gambling offer, which may in turn facilitate excessive gambling behaviours. This study compares establishments that possess a single license to operate EGMs with those that are multi-licensed regarding four environmental risk factors: advertisements, automated teller machines, isolated gambling area, and EGM operating hours. A field observation was carried out by seven pairs of observers in 166 establishments in Capitale-Nationale de Quebec (QC), Canada. In each establishment, observers had to complete an observational grid on an iPod touch to gather environmental data related to the identified variables. Results from the stepwise logistic regression show that being a multi-licensed establishment increases the chance of having longer operating hours and displaying non-regulated advertisements that promote gambling. Multi-licensed establishments tend to offer a more attractive gambling environment, which may increase the risk of excessive gambling. Reinforcement of regulations for responsible gambling is discussed.RésuméPour les établissements ayant un permis d’alcool, posséder une licence d’exploitation d’appareils de jeux électroniques (AJE) se traduit par un revenu complémentaire. De ce fait, les détaillants ayant plus d’une licence d’exploitation d’AJE peuvent tirer parti de la promotion de leur offre de jeu, ce qui, en retour, peut encourager des comportements de jeu excessifs. Cette étude compare les établissements qui détiennent une seule licence d’exploitation d’AJE avec ceux qui en détiennent plusieurs en tenant compte de quatre facteurs de risque environnementaux : la publicité, les guichets automatiques, la zone de jeu isolée et les heures d’exploitation des AJE. Une observation sur le terrain a été effectuée par sept paires d’observateurs dans 166 établissements de Capitale-Nationale de Québec (QC), Canada. Dans chaque établissement, ils ont recueilli des données environnementales liées aux variables identifiées qu’ils ont compilées dans une grille d’observation sur un iPod touch. Les résultats du modèle logistique utilisant la régression séquentielle montrent que le fait, pour un établissement, d’avoir plusieurs licences augmente la possibilité de prolonger les heures d’exploitation et d’afficher des publicités non réglementées qui font la promotion du jeu. Les établissements ayant plusieurs licences ont tendance à offrir un environnement de jeu plus attrayant, ce qui peut accroître le risque de jeu excessif. Un resserrement des règles pour une réglementation responsable du jeu est à l’étude.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.259
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.428
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes3
Has abstractyes

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