The Determinants of Adherence to Public Health and Social Measures Against COVID-19 Among the General Population in South Korea: National Survey Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has created devastating health, social, economic, and political effects that will have long-lasting impacts. Public health efforts to reduce the spread of COVID-19 are the priority of national policies for responding to the pandemic globally. Public health and social measures (PHSMs) have been shown to be effective when used alone or in combination with other measures, reducing the risk of spreading COVID-19. However, there is insufficient evidence on the status of compliance with PHSMs in the general population for the prevention of COVID-19 in public areas, including Korea. OBJECTIVE: The aim of this study was to assess levels of compliance with the recommended PHSMs against SARS-CoV-2 infection and their predictors among the general population by using national data. METHODS: This study was a secondary data analysis of the National Survey of Infectious Disease Preventive Behaviors in Community, which was conducted by the Korea Centers for Disease Control and Prevention Agency (KDCA) between October 12 and October 30, 2020. The primary study was cross-sectional, using stratified sampling via an adjusted proportional allocation method to select representative samples and ensure the stability of samples. The data were collected through phone interviews conducted by trained enumerators using a structured questionnaire. PHSM adherence was measured using a 10-item comprehensive infectious disease prevention behavior (CIDPB) scale, and each sociocognitive factor, including perceived susceptibility to SARS-CoV-2 infection, perceived severity of SARS-CoV-2 infection, perceived confidence in performing preventive behaviors related to COVID-19, information comprehension ability, and trust in information from the KDCA, was measured. A total of 4003 participants were included in the final analysis. Tobit regression and a decision tree analysis were performed to identify the predictors of preventive measures and the target groups for intervention. RESULTS: We discovered that women scored 1.34 points higher on the CIDPB scale than men (P<.001). Compared to the group aged 19 to 29 years, those aged 50 to 59 years and those older than 60 years scored 1.89 and 2.48 points higher on the CIDPB scale (P<.001), respectively. The perceived severity of infection, confidence in preventive behaviors, information comprehension ability, and trust in information from the KDCA were significant positive determinants of CIDPBs (P<.001). The perceived susceptibility to infection showed a significant negative relationship with CIDPBs (P<.001). CONCLUSIONS: Female sex, older age, lower income, and sociocognitive factors were found to be significant determinants of adhering to PHSMs. The findings suggest the need for tailored interventions for target groups; specifically, the age group that was the most active at work indicated the highest potential to spread infection. Adequate public health education and health communication for promoting adherence to PHSMs should be emphasized, and behavior change strategies for those with low perceived confidence in performing PHSMs should be prioritized.
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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 | 0,002 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,001 | 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 ».