Alcohol consumption during the COVID‐19 pandemic in Europe: a large‐scale cross‐sectional study in 21 countries
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Notice bibliographique
Résumé
AIMS: To investigate changes in alcohol consumption during the first months of the COVID-19 pandemic in Europe as well as its associations with income and experiences of distress related to the pandemic. DESIGN: Cross-sectional on-line survey conducted between 24 April and 22 July 2020. SETTING: Twenty-one European countries. PARTICIPANTS: A total of 31 964 adults reporting past-year drinking. MEASUREMENTS: Changes in alcohol consumption were measured by asking respondents about changes over the previous month in their drinking frequency, the quantity they consumed and incidence of heavy episodic drinking events. Individual indicators were combined into an aggregated consumption-change score and scaled to a possible range of -1 to +1. Using this score as the outcome, multi-level linear regressions tested changes in overall drinking, taking into account sampling weights and baseline alcohol consumption [Alcohol Use Disorder Identification Test (AUDIT-C)] and country of residence serving as random intercept. Similar models were conducted for each single consumption-change indicator. FINDINGS: The aggregated consumption-change score indicated an average decrease in alcohol consumption of -0.14 [95% confidence interval (CI) = -0.18, -0.10]. Statistically significant decreases in consumption were found in all countries, except Ireland (-0.08, 95% CI = -0.17, 0.01) and the United Kingdom (+0.10, 95% CI = 0.03, 0.17). Decreases in drinking were mainly driven by a reduced frequency of heavy episodic drinking events (-0.17, 95% CI = -0.20, -0.14). Declines in consumption were less marked among those with low- or average incomes and those experiencing distress. CONCLUSIONS: On average, alcohol consumption appears to have declined during the first months of the COVID-19 pandemic in Europe. Both reduced availability of alcohol and increased distress may have affected consumption, although the former seems to have had a greater impact in terms of immediate effects.
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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 | 0,001 | 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) | 0,002 | 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 découle