The Effectiveness of Improving Infectious Disease–Specific Health Literacy Among Residents: WeChat-Based Health Education Intervention Program
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".