Impacts of the COVID-19 pandemic on gamblers in Canada: Qualitative analysis of responses to an open-ended question
Notice bibliographique
Résumé
CONTEXT: Many stakeholders have expressed concerns about the impacts of the COVID-19 pandemic on gambling practices. These have historically increased during crises, potentially leading to deleterious effects on problematic gamblers, their families, and their communities. Primary care professionals need to better understand gamblers' experience during the pandemic to offer high level of care for this population. OBJECTIVE: draw up a portrait of the experience of gamblers regarding the impacts of the COVID-19 pandemic on their gambling practices. STUDY DESIGN: Qualitative analysis. SETTING: Content analysis of responses to a single open-ended question placed at the end of a cross-sectional survey which was online from February 16 to March 15 2021 in Quebec (Canada). Participants were recruited by a non-randomised online sampling. POPULATION STUDIED: 1529 individuals participated in the study, of whom 724 answered the open-ended question. Inclusion criteria were: (1) 18 years and older (2) living in the province of Quebec, Canada (3) has gambled at least once in the past year. RESULTS: Respondents' median age is 43 years, 54% are women and 57% are problem gamblers according to the Problem Gambling Severity Index. Three main themes were identified: (1) the changes in gambling practices during the pandemic as perceived by the respondents, (2) the impacts of these changes on their lives, and (3) the factors that influenced these changes. A significant proportion of gamblers felt that their gambling practices had increased during the pandemic, mainly due to boredom and increased free time. Many of them did not report deleterious effects of this increase whereas others reported being devastated. On the opposite, the pandemic was perceived by some participants as a unique window of opportunity to decrease their problematic gambling practices. CONCLUSIONS: The pandemic has created space to fill into many individuals' lives as usual leisure activities, hobbies and spending habits became out of reach. It led to increased gambling for many participants. While many did not report deleterious effects of this increase, others expressed being at great risk and therefore need primary care professionals to be equipped to support them.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».