The Relationship Between Health Literacy, Knowledge, Fear, and COVID-19 Prevention Behavior in Different Age Groups: Cross-sectional Web-Based Study
Notice bibliographique
Résumé
BACKGROUND: COVID-19 prevention behaviors have become part of our lives, and they have been reported to be associated with health literacy, knowledge, and fear. However, the COVID-19 pandemic may be characterized by different situations in each age group. Since the severity of the infection and the means of accessing information differ by age group, the relationship between health literacy, knowledge, and fear may differ. Thus, factors that promote preventive behavior may differ by age group. Clarifying the factors related to prevention behaviors by age may help us consider age-appropriate promotion. OBJECTIVE: This study aims to examine the association between COVID-19 prevention behaviors and health literacy, COVID-19 knowledge, and fear of COVID-19 by age group. METHODS: A cross-sectional study was conducted among 512 participants aged 20-69 years, recruited from a web-based sample from November 1 to November 5, 2021. A web-based self-administered questionnaire was used to obtain the participants' characteristics, COVID-19 prevention behaviors, health literacy, COVID-19 knowledge, and fear of COVID-19. The Kruskal-Wallis rank sum test was used to compare the scores of each item for each age group. The relationships among COVID-19 prevention behaviors, health literacy, COVID-19 knowledge, and fear of COVID-19 were analyzed using the Spearman rank correlation analysis. Additionally, multiple regression analysis was conducted with COVID-19 prevention behaviors as dependent variables; health literacy, COVID-19 knowledge, and fear of COVID-19 as independent variables; and sex and age as adjustment variables. RESULTS: For all participants, correlation and multiple regression analyses revealed that prevention behaviors were significantly related to health literacy, COVID-19 knowledge, and fear of COVID-19 (P<.001). Additionally, correlation analysis revealed that fear of COVID-19 was significantly negatively correlated with COVID-19 knowledge (P<.001). There was also a significant positive correlation between health literacy and COVID-19 knowledge (P<.001). Furthermore, analysis by age revealed that the factors associated with prevention behaviors differed by age group. In the age groups 20-29, 30-39, and 40-49 years, multiple factors, including health literacy, influenced COVID-19 prevention behaviors, whereas in the age groups 50-59 and 60-69 years, only fear of COVID-19 had an impact. CONCLUSIONS: The results of this study revealed that the factors associated with prevention behaviors differ by age. Age-specific approaches should be considered to prevent infection.
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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 | 0,001 | 0,003 |
| 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 | 0,000 |
| 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) | 0,002 | 0,001 |
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 ».