Changes in Infectious Disease–Specific Health Literacy in the Post–COVID-19 Pandemic Period: Two-Round Cross-Sectional Survey Study
Bibliographic record
Abstract
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".