MétaCan
Menu
Retour à la cohorte
Enregistrement W2767399693 · doi:10.1111/add.13958

Commentary on van der Maas <i>et al</i>. (2017): Going where the action is

2017· letter· en· W2767399693 sur OpenAlexaboutno aff
Francis Markham, Martin Young

Notice bibliographique

RevueAddiction · 2017
Typeletter
Langueen
DomainePsychology
ThématiqueGambling Behavior and Treatments
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHarmPsychologyPopulationDemographyPsychiatrySocial psychologySociology

Résumé

récupéré en direct d'OpenAlex

Approximately three in 10 patrons leaving electronic gaming machine venues report serious gambling-related harm. Public health surveillance and harm minimization measures should be focused on these venues, because this is where problem gamblers are spatially concentrated. In an admirable study of casino bus tours in Ontario, van der Maas et al. 1 took the unusual step of going—as Erving Goffman might have put it—‘where the action is’ 2. The authors recruited study participants by undertaking venue exit surveys (i.e. intercept surveys) outside six ‘racinos’ and one casino in Ontario, Canada. They surveyed a stratified sample of venue-goers who were resident in Ontario and aged more than 54 years, and included the Problem Gambling Severity Index (PGSI) as a primary outcome measure. This survey revealed an extraordinarily high prevalence (28.8%) of combined problem and moderate risk gambling (PGSI 3 or more). This compares to an estimate of only 2.5% of adults in the general population of Ontario on the same measure 3. In addition, approximately 30% of the remaining gamblers intercepted by van der Maas et al. reported PGSI scores of 1 or 2. Only four in 10 patrons leaving these Ontario venues reported no symptoms of problem gambling. More troubling still, these data are likely to underestimate the true prevalence of problem gambling among electronic gaming machine (EGM) players. First, not all patrons who visit a casino or racino gamble on EGMs every visit. Problem gambling prevalence among those who did play EGMs during their visit is likely to be higher than 28.8%. Secondly, the van der Maas et al. sample included only older Ontario residents, a subpopulation known to have relatively low rates of gambling problems (e.g. 3, 4). Thirdly, problem gamblers play EGMs for longer than non-problem gamblers 5, meaning that an intercept survey will oversample non-problem gamblers relative to the population of those playing EGMs in these Ontario venues. Finally, problem gamblers are disinclined to respond honestly to surveys of this kind 6. In short, the van der Maas et al. PGSI 3+ estimate is likely to be very conservative. Van der Maas et al.'s findings are consistent with estimates of ‘problem gambling time shares’ derived using different methods. For example, in one study, Rodgers et al. asked respondents to a telephone survey how frequently they gambled on EGMs, how long they gambled for and their responses to the PGSI 7. Population-weighted bootstrap methods were used to estimate how many of the minutes spent gambling on EGMs were accounted for by problem gamblers. This study found that 36.8% [95% confidence interval (CI) = 27.3–49.5%) of minutes gambling on EGMs were contributed by those scoring 3 or more on the PGSI, and 65.8% (95% CI = 53.0–81.6%) of minutes were contributed by those scoring 1 or more on the PGSI. The alarmingly high prevalence of problem gambling within gambling venues has important implications. First, if approximately three in 10 gamblers within an EGM venue at any given time report gambling problems, then ‘responsible gambling’ codes of conduct for gambling venues should be radically reconsidered. For EGM venues obliged to intervene in cases of problem gambling 8, compliance would require interventions with at least every third EGM gambler. Secondly, the ubiquity of problem gambling in EGM venues suggests that harm minimization measures must be venue-orientated. However, there is currently little evidence evaluating the effectiveness of EGM harm reduction measures 9. Development and evaluation of in-venue harm reduction measures for EGMs needs to be a research priority. Thirdly, the van der Maas et al. study suggests that EGM venues should be a key site of public health surveillance. The monitoring of problem gambling via general population surveys is hampered by the small proportion of the general population who report gambling problems 10, 11. Monitoring EGM venues—the locations where problem gamblers can be found most easily—presents a promising opportunity for public health surveillance. Fourthly, the fact that problem gamblers are concentrated in EGM venues suggests that recruitment for treatment programs should focus on these spaces. Finally, as businesses, EGM venues are likely to resist scrutiny by researchers. Regulators need to consider the imposition of mandatory research participation as part of the licensing conditions of venues. Problem gamblers are concentrated heavily within EGM venues. Van der Maas et al. found that three in 10 patrons of EGM venues report significant problem gambling symptoms. Researchers and regulators should focus their efforts on EGM venues—the places ‘where the [problem gambling] action is’. F.M. has received funding from, or been employed on projects that received funding from the Australian Research Council, the Community Benefit Fund of the Northern Territory and the Australian Capital Territory Gambling and Racing Commission. His travel expenses to speak at an international conference have been paid by the Alberta Gambling Research Institute, an organization funded by the provincial government of Alberta. He is a member of the Public Health Association of Australia. M.Y. has received funding from the Australian Research Council, Gambling Research Australia and several Australian state government departments, most notably the Community Benefit Fund of the Northern Territory Government. Neither author has received funding from the gambling, tobacco or alcohol industries, nor are there any constraints on the publication of this paper.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,019
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,101
Tête enseignante GPT0,396
Écart entre enseignants0,295 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations1
Publié2017
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueAddictionMême sujetGambling Behavior and TreatmentsTravaux en français237 207