A Moderated Mediation Model Explaining the Relationship Between Risk-Group Membership, Threat Perception, Knowledge, and Adherence to COVID-19 Behavioral Measures
Notice bibliographique
Résumé
Background: COVID-19 is a threat to individual and global health, thus, reducing the disease's spread is of significant importance. However, adherence to behavioral measures against the spread of COVID-19 is not universal, even within vulnerable populations who are at higher risk of exposure to the virus or severe COVID-19 infection. Therefore, this study investigates how risk-group membership relates to adherence to COVID-19 behavioral measures, whether perceived threat of COVID-19 is a mechanism explaining this relationship, and whether knowledge about COVID-19 moderates these effects. Methods: = 4,096) representative of the adult population in Germany with regard to gender, age (18 to 74), and province. Therein, we assessed risk group membership with two indicators (risk of exposure to COVID-19 and risk of severe COVID-19 infection), perceived COVID-19 threat with the Perceived Coronavirus Threat Questionnaire, knowledge about COVID-19 with a knowledge test; and adherence to six behavioral measures to protect against the spread of COVID-19 (e.g., keeping distance, using mouth-nose protection, and following contact restrictions). We used moderated mediation models to test whether perceived threat mediates the relationship between risk-group membership and adherence and whether knowledge about COVID-19 moderates this relationship. Results: We found that risk group members had more perceived COVID-19 threat and that knowledge about COVID-19 increased perceived threat. Moreover, risk group membership had a positive direct effect on adherence to most behavioral measures and risk group members with less knowledge about COVID-19 violated measures more frequently. Risk-group membership also had positive indirect effects on adherence via perceived COVID-19 threat. The moderated indirect effects of threat indicate that threat led to more adherence when knowledge was low, but lost relevance as knowledge increased. Conclusion: The results may help to evaluate disease-regulation measures and to combat the pandemic more effectively. For example, increasing COVID-19 knowledge in the general population could increase adherence to COVID-19 behavioral measures. However, policy makers should be mindful that this could also have negative mental health implications as knowledge increases perceived COVID-19 threat.
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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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».