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Record W2972348902 · doi:10.4309/jgi.2019.42.3

Hey Big Spender: An Ecological Momentary Assessment of Sports and Race Betting Expenditure by Gambler Characteristics

2019· article· en· W2972348902 on OpenAlexvenueno aff
Nerilee Hing, Alex MT Russell, Anna Thomas, Rebecca Jenkinson

Bibliographic record

VenueJournal of Gambling Issues · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsHarmRace (biology)PsychologyActuarial scienceSocial psychologyEconomicsSociology

Abstract

fetched live from OpenAlex

A major obstacle to understanding how expenditure varies amongst people who gamble is the difficulty of obtaining accurate expenditure data from individual gamblers. To overcome the shortcomings of retrospective self-reports, this study used a prospective ecological momentary assessment (EMA) design to capture these data every 24/48 hours. It aimed to examine 1) demographic, psychological, behavioural and contextual characteristics of high-spending sports and race bettors, and 2) the relationship between betting outlay and problem gambling severity. A baseline survey was completed by 320 regular sports bettors and 402 regular race bettors, followed by 15 EMA surveys over three non-consecutive weeks. Higher spending bettors were more likely to: be male, place more of their bets online, have higher disposable incomes, have commenced betting at a younger age, have more accounts with betting operators, and bet when affected by alcohol. The analyses confirmed the strong link between problem gambling severity and financial outlay on betting. Regular sports bettors experiencing gambling problems spent four times more, and those at moderate-risk spent three times more, than their non-problem gambling counterparts. Regular race bettors experiencing gambling problems spent three times more, and those at moderate-risk spent twice as much, as the non-problem gambling race bettors. These results suggest that regulatory and other initiatives that help bettors to limit or reduce their financial outlay on betting should be central to harm minimisation efforts, in order to reduce the growing number of bettors experiencing gambling problems and harm. Résumé Un des principaux obstacles à la compréhension de la variation des dépenses entre les joueurs est la difficulté d’obtenir des données précises sur les dépenses de la part de joueurs individuels. Pour pallier les faiblesses d’auto-évaluations rétrospectives, cette étude visait à utiliser un modèle d’évaluation écologique momentanée (EMA) prospective pour saisir ces données toutes les 24 à 48 heures, afin d’examiner 1) les caractéristiques démographiques, psychologiques, comportementales et contextuelles de gros parieurs de course et de paris sportifs et 2) la relation entre les dépenses de paris et la gravité du jeu problématique. Une enquête initiale a été réalisée auprès de 320 parieurs sportifs et de 402 parieurs de course réguliers, suivie de 15 sondages EMA sur trois semaines non consécutives. Les plus gros parieurs étaient plus susceptibles de: placer davantage de paris en ligne, d’avoir un revenu disponible plus élevé, d’avoir commencé à parier à un plus jeune âge, d’avoir davantage de comptes auprès d’opérateurs de paris et de parier sous l’influence de l’alcool. Les analyses ont confirmé le lien étroit qui existe entre la gravité du jeu problématique et les dépenses financières consacrées aux paris. Les parieurs sportifs réguliers aux prises avec des problèmes de jeu dépensaient quatre fois plus et ceux à risque modéré, trois fois plus, que leurs homologues sans problème de jeu. Les parieurs de course réguliers aux prises avec des problèmes de jeu dépensaient trois fois plus et ceux à risque modéré, deux fois plus, que leurs homologues sans problème de jeu. Ces résultats laissent entrevoir que les initiatives réglementaires et autres initiatives qui aident les parieurs à limiter ou à réduire leurs dépenses en paris devraient être au cœur des efforts de minimisation des préjudices, afin de réduire le nombre croissant de parieurs ayant des problèmes de jeu et de préjudices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.148
GPT teacher head0.440
Teacher spread0.293 · 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.

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

Citations11
Published2019
Admission routes1
Has abstractyes

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