Psychological and Demographic Determinants of Substance Use and Mental Health During the COVID-19 Pandemic
Notice bibliographique
Résumé
Background: Alcohol consumption and distress have increased among Canadians since the start of the COVID-19 pandemic. Methods: We examined whether sociodemographic and COVID-19-related worries are associated with various combinations of alcohol consumption and comorbid psychological distress variables among a Canadian sample of adults. Data were derived from a sample of Canadian adults ( N = 1,005, 49.6% female) who participated in an online survey in May 2020. Four multivariate ordinal logistic regression models were used to estimate the odds of binge drinking, increased alcohol consumption during the pandemic, and psychological distress. Predictor variables used in the analyses included self-reported sociodemographic characteristics, financial worries, COVID-19 impact on work, and worrying about getting ill. Results: Women were found to have higher odds of increased drinking and anxiety. Also being divorced, separated, or widowed was associated with higher odds of binge drinking and anxiety, and binge drinking and depression. Furthermore, being 60 or older was associated with lower odds of binge drinking and depression and increased drinking and depression, as well as lower odds of increased drinking and depression and increased drinking and anxiety. High income groups were associated with higher odds of binge drinking, increased drinking, and mental distress. Compared to those less worried, being very worried about finances were associated with higher odds of binge drinking and anxiety, increased drinking and anxiety, and increased drinking and depression. Also, being very worried about getting ill with COVID was associated with higher odds of binge drinking and anxiety and increased drinking and anxiety. Conclusion: Our findings identify several demographic and COVID-related worries for increased odds of alcohol intake and co-morbid psychological distress during the COVID-19 pandemic, including identifying as a woman, high income groups, being divorced, separated or widowed, and experiencing financial worries and COVID illness worries. These characteristics should be considered when developing prevention and treatment programs for adults with problematic alcohol use and comorbid anxiety and depression.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,000 | 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 ».