Changes in Infectious Disease–Specific Health Literacy in the Post–COVID-19 Pandemic Period: Two-Round Cross-Sectional Survey Study
Notice bibliographique
Résumé
Background: Infectious disease-specific health literacy (IDSHL) is a crucial factor in the development of infectious diseases. It plays a significant role not only in mitigating the resurgence of infectious diseases but also in effectively averting the emergence of novel infections such as COVID-19. During the 3 years of the COVID-19 pandemic, China primarily adopted nonpharmaceutical interventions, advocating for people to avoid crowded places and wear masks to prevent the spread of COVID-19. Consequently, there has been a dearth of research concerning IDSHL and its corresponding focal points for health education. Objective: This study aimed to (1) evaluate the changes in IDSHL scores between 2019 (before the COVID-19 pandemic) and 2022 (the postepidemic period of COVID-19) and (2) explore the risk factors affecting IDSHL using a multivariate logistic regression analysis. Methods: This study used 2-round cross-sectional surveys, conducted in 2019 and 2022, respectively, in 30 counties in Zhejiang Province, China. Multiple-stage stratified random sampling was used to select households, and a Kish grid was used to identify participants. An identical standardized questionnaire consisting of 12 closed-ended questions was used to measure IDSHL scores before and after the COVID-19 pandemic (2019 and 2022). Standard descriptive statistics, chi-square tests, t tests, and multivariate logistic regression analyses were used to analyze the data. Results: The 2-round cross-sectional surveys conducted in 2019 and 2022 yielded, out of 19,366 and 19,221 total questionnaires, 19,257 (99.44% response rate) and 18,857 (98.11% response rate) valid questionnaires, respectively. The correct response rate for the respiratory infectious diseases question "When coughing or sneezing, which of the following is correct?" increased from 29.10% in 2019 to 37.92% in 2022 (χ²1=332.625; P<.001). The correct response rate for the nonrespiratory infectious diseases question "In which of the following ways can hepatitis B be transmitted to others?" decreased from 64.28% to 59.67% (χ²1=86.059; P<.001). In terms of IDSHL scores, a comparison between 2022 and 2019 revealed notable statistical differences in the overall scores (t1=10.829; P<.001) and across the 3 dimensions of knowledge (t1=8.840; P<.001), behavior (t1=16.170; P<.001), and skills (t1=9.115; P<.001). With regard to the questions, all but 4 exhibited statistical differences (P<.001). Multivariate logistic regression analyses indicated that the 2022 year group had a higher likelihood of possessing acquired IDSHL than the 2019 group (odds ratio 1.323, 95% CI 1.264-1.385; P<.001). Conclusions: When conducting health education, it is imperative to enhance efforts in nonrespiratory infectious disease health education, as well as respiratory infectious diseases such as COVID-19. Health education interventions should prioritize ethnic minority populations with a poor self-health status and low education.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| 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 | 0,001 | 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 ».