The Effectiveness of Improving Infectious Disease–Specific Health Literacy Among Residents: WeChat-Based Health Education Intervention Program
Notice bibliographique
Résumé
Background Infectious disease–specific health literacy (IDSHL) has become an important determinant of infectious disease incidence. It can not only reduce the incidence of re-emerging infectious diseases, but also effectively prevent the emergence of new infectious diseases such as COVID-19. WeChat, as a new media, has been proven to greatly reduce the chance of infectious diseases spreading from person to person, especially in case of respiratory infections. However, there is currently no concrete health education invention program to improve IDSHL using a WeChat public account. Objective The aims of this study were as follows: (1) to determine the IDSHL of the population in Zhejiang, China; (2) to develop a health education program for the improvement of IDSHL using a WeChat public account; and (3) to evaluate the effectiveness of the health education program that was implemented in the prevention of infectious disease outbreaks. Methods We used a standardized questionnaire, which consisted of 28 closed-ended questions, to measure the level and score of IDSHL before and after intervention. A multiple-stage stratified random sampling technique was used to select study participants from Zhejiang province in China, who were further divided randomly into 2 groups: the intervention and control groups. From July 2014 to January 2015, a WeChat-based health education intervention program was carried out on the intervention group. Standard descriptive statistics and chi-square and t tests were conducted to analyze the data. Results A total of 3001 residents participated in the baseline survey of this study. At baseline, participant IDSHL rates were 73.29% and 72.12% for the intervention and control groups, respectively (Χ21=0.5; P=.50). After 7 months of intervention, 9.90% (297/3001) of participants dropped out of the study. Of the lost participants, 119 were from the intervention group and 178 were from the control group. There were significant differences between follow-up and lost participants with respect to age (P=.04), marital status (P=.02) and occupations (P=.002). After intervention, the intervention group scores in the different domains were higher than those in the control group (infectious disease–related knowledge, prevention, management, or treatment, identification of pathogens and infection sources, and cognitive ability). There were significant improvements in the IDSHL of participants in both the intervention and control groups (Χ21=135.9; P<.001 vs Χ21=9.1; P=.003), and there was a greater change in the IDSHL among the intervention group participants than among the control group participants (1230/1359, 90.51% vs 1038/1359, 77.17%). Conclusions The health education intervention program using a WeChat public account proved to be an effective, feasible, and well-accepted means to improve the IDSHL of the general population. In the future, this health education intervention program can be used as a reference for prevention and treatment of infectious diseases.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 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) | 0,002 | 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 ».