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Enregistrement W4414758565 · doi:10.20429/amtp.2011.43

Dimensions of Problem Gambling Behavior Associated with Purchasing Sports Lottery

2011· article· en· W4414758565 sur OpenAlexaboutno aff
Hai Li, Luke Lunhua Mao, James J. Zhang

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLotteryPurchasingImpulse control disorderSocial issuesConsumption (sociology)China

Résumé

récupéré en direct d'OpenAlex

Sport gambling is a major segment of the sport industry. Although various forms of sport gambling may help generate revenues, increase governmental tax income, and advance social and economic development, it has the potential to cause social, family, and individual problems, thus imposing cost and burden to a community. In recent years, problem gambling associated with purchasing sports lottery has gradually emerged as a major social issue in China. Problem gambling is often referred as all patterns of gambling behavior that compromise, disrupt, or damage personal, family, or vocational pursuits (Lesieur, 1988). From this perspective, pathological gambling can be regarded as a sub-category, or one end of a continuum, of gambling-related problems. The term of problem gambling can also be used to denote a level of gambling, ranging from an early stage of on-site problems to compulsive or pathological gambling as diagnosed by applying the DSM-IV diagnostic instrument (Moore, 2002; Raylu & Oei, 2002; Rosenthal, 1989; Volberg, 2001). When people refer to problem gambling, it typically means subclinical level of gambling problems. While many studies have been conducted on general problem gambling, few have focused on issues that are primarily caused by sports betting. Furthermore, previous studies on problem gambling have mainly been conducted in western countries, such as the U.S., Australia, Britain, and Canada; research findings from these studies are limited in their applicability to China due to historical, social, and cultural differences. For instance, the government-run sports lottery in China has existed for merely 20 years and because of the absence of other gambling forms in mainland China, sports lottery is the primary outlet of gambling with a large participation rate among Chinese residents. Understanding the fundamental issues caused by sports lottery is precursor to formulating effective strategies for prevention, intervention, or even rehabilitation. The purpose of this study was to identify and examine the dimensions of problem gambling behaviors associated with purchasing sports lottery in China. This was accomplished through the development and validation of the initial Scale of Assessing Problem Gambling (SAPG). The SAPG was initially developed through conducing a comprehensive review of literature (e.g., Custer, 1982; Lesieur & Blume, 1987), conducting open-ended interviews of gamblers (n = 20), administrators of sports lottery programs (n = 40), and retail store managers (n = 20), and summarizing telephone transcripts of a hotline for problem gambling. Through these procedures, a total of 45 items were identified, which was submitted to a panel of experts in gambling research or administration (n = 9) for a test of content validity. A total of 32 items were retained and phrased into a Likert 5-point scale. A survey packet was prepared that contained an informed consent letter, the SAPG items, and demographic variables. Research participants (N = 4,991) were Chinese residents who have purchased sports lottery tickets in the past 12 month. Test administration was carried out in five large cities (Shenyang, Shanghai, Guangzhou, Chengdu and Zhengzhou) with population ranging from 7 million to 15 million. These cities represented the largest lottery market in five geographical regions of China. Within each city, a computer-generated, randomly stratified multistage sampling method was used to survey one out of 10 thousands of local adult population. Following the survey packet, one-on-one interviews was conducted at retailing stores of sports lottery by trained investigators. A total of 5,450 interviews were conducted, and 4,991 participants were willing to complete the survey, representing a response rate of 92%. A sports lottery ticket was used as an incentive to complete the study. Of the respondents, 77.3% were male and 22.7% female. In terms of occupation, 39.1% were professionals,, 20.4% unemployed or retired, 19.3% peasant workers, 12.1% private business owners, and 8.5% students or others. A majority of the participants (i.e., 93%) ranged in age between 20 and 60 years old. Due to the exploratory nature of this study, the total sample was randomly split into two halves (Lattin, Carroll, and Green 2003). The first half was used for conducting an EFA and the second half for a CFA. Both EFA and CFA were examined using weighted least-square means and variance adjusted (WLSMV) estimates, following the procedures in Mplus 5.12 (Muthen and Muthen, 2006). The ‘goodness-of-fit’ approach was adopted to determine the number of factors in the EFA (Fabrigar et al. 1999). An EFA with Geomin for oblique rotation revealed that a four-factor solution was optimal (RMSEA=.04, TLI=.99, CFI=.96), including Harmful Behaviors (9 items), Compulsive Disorders (6 items), Over Expectation (2 items), and Depression Sign (2 items). All factors had low to moderate inter-item correlation (.18-.69). As the EFA findings were data driven, an in-depth examination of content interpretability of the factors revealed that the Harmful Behavior factor contained two distinct concepts and should thus be split into two factors: Social Consequence (4 items) and Financial Consequence (5 items). In the CFA, the five-factor model showed good fit to the data (RMSEA = .050, TLI=.978, and CFI=.922). All factors again showed low to moderate inter-item correlation with each other (.29-.60). Conducting a comparison with the original four-factor structure (RMSEA = .052, TLI=.976, and CFI=.915), the level of model fit for the five-factor structure was significantly (p < .05) superior (χ24 = 65.03). As the Over Expectation and Depression Sign factors had only two items, they were of mediocre composite reliability coefficients (.60 and .72) and AVE values (.41 and .43). The composite reliability coefficients for the remaining three factors were greater than .80 and AVE values greater than .50, respectively, indicating good convergent validity. Consequently, the SAPG scale with a total of 19 items under five factors (i.e., Social Consequence, Financial Consequence, Harmful Behavior, Compulsive Disorder, and Depression Sign) was formulated with good measurement properties to assess problem gambling of sports lottery consumers in China. This study represents an initial effort to understand the dimensions of problem gambling associated with Chinese sports lottery. The developed scale may be adopted by researchers and practitioners to examine problem gambling behaviors and develop effective prevention and intervention procedures based on tangible evidence. With proper adaption, modification, and validation, the SAPG has the potential to be adopted in other socio-cultural contexts, such as North America, where behavioral problems associated with sport gambling likely exist among sport gamblers and yet, few specific measurement tools are available.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,014

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,143
Tête enseignante GPT0,354
Écart entre enseignants0,211 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2011
Routes d'admission1
Résumé présentoui

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