Cognitive Factors Associated With Public Acceptance of COVID-19 Nonpharmaceutical Prevention Measures: Cross-sectional Study
Notice bibliographique
Résumé
Background: During the COVID-19 crisis, protests against restrictions emerged and rule violations increased, provoking peaks in new positive cases, forcing authorities in France to impose fines to slow down the spread of the disease. Due to these challenges, subsequent implementations of preventive measures in response to COVID-19 recurrences or other pandemics could present difficulties for decision makers. A better understanding of the factors underlying the public acceptance of COVID-19 nonpharmaceutical preventive measures may therefore contribute greatly to the design of more effective public communication during future pandemics. Objective: The aim of this study was to evaluate the acceptance of COVID-19 nonpharmaceutical prevention measures in France. The specific objectives were (1) to examine the public's acceptance of COVID-19 nonpharmaceutical prevention measures and (2) to assess the association of the public's acceptance of these prevention measures and their perception of COVID-19. Methods: Data were collected from 2004 individuals through an online survey conducted 6-8 weeks after the first lockdown in France. For objective 1, participants were asked the extent to which they supported 8 COVID-19 nonpharmaceutical preventive measures using a 4-point Likert scale. For objective 2, COVID-19-related perceptions were assessed using a 5-point Likert scale from an adapted version of Witte's Extended Parallel Process Model. Sociodemographic and environmental variables were also collected. The public's acceptance factors were estimated using an unweighted least squares factorial analysis, and their associations with perceptions of COVID-19, expressed as rate ratios (RR) and 95% CIs, were estimated using generalized linear Poisson regression models. Statistical analyses were performed using the SPSS statistical package. Results: The acceptance rate reached 86.1% for individual protective measures, such as making masks mandatory in public open spaces, and 70.0% for collective restrictions, such as isolating the most vulnerable people (1604/2004, 80%) or forbidding public gatherings (n=1590, 79.3%). The least popular restrictions were closing all schools/universities and nonessential commerce such as bars and restaurants (n=1146, 57.2%). Acceptance of collective restrictions was positively associated with their perceived efficacy (RR 1.02, 95% CI 1.01-1.03), fear of COVID-19 (RR 1.04, 95% CI 1.03-1.05), and perceived severity of COVID-19 (RR 1.04, 95% CI 1.03-1.06), and negatively with age >60 years (RR 0.89, 95% CI 0.81-0.98). Acceptance of individual protective measures was associated with their perceived efficacy (RR 1.03, 95% CI 1.03-1.04), fear of COVID-19 (RR 1.02, 1.01-1.03), and perceived severity of COVID-19 (RR 1.03, 1.01-1.05). Conclusions: Acceptance rates of COVID-19 nonpharmaceutical measures were rather high, but varied according to their perceived social cost, and were more related to collective than personal protection. Nonpharmaceutical measures that minimize social costs while controlling the spread of the disease are more likely to be accepted during pandemics.
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 | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».