A portrait of online gambling: a look at a transformation amid a pandemic
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic brought about an extraordinary societal context in which the gambling offer was modified to meet public health measures intended to curb viral transmission. With many land-based gambling venues being forced to close, gambling opportunities were left almost exclusively to the online domain, thus possibly instigating changes in the population's online gambling habits. Using a sequential mixed methods design, this study aimed to (1) investigate the self-reported changes in gambling habits of adults in the province of Québec (Canada) following the declaration of the COVID-19 pandemic and ensuing public health responses, and (2) report on their lived experiences of these changes during the first year of the pandemic. METHOD: A population survey was conducted with a representative sample of 4,676 online gamblers residing in the province of Québec, which was selected through random digit dialing for telephone interviews and from a web panel. From the initial sample, 96 online gamblers were recruited for in-depth semi-structured interviews inquiring about their gambling experiences during the first year of the pandemic. RESULTS: The prevalence of online gambling was estimated at 15.6-20.3% of Québec's population in 2021, among which 5.6% gambled online for the first time during the pandemic, which represented a substantial addition to the 14.7% of people who gambled online both before and during the pandemic. Only 1.4% of people quit online gambling during the pandemic. The impact of the pandemic was similar for frequency, expenditure, and time spent on various online gambling activities, with day trading having increased most during the pandemic. Seeking to earn money was one of several motivations endorsed by participants who had begun or increased online gambling practices during the first year of the pandemic. CONCLUSION: The COVID-19 pandemic clearly revealed a significant increase in online gambling practices when changes in the gambling landscape and in daily life occurred due to the health crisis. This calls for a greater attention to the need for comprehensive regulatory measures and a support system for online gambling in a context of a steadily increasing lucrative market.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».