The Relationship Between Health Literacy, Knowledge, Fear, and COVID-19 Prevention Behavior in Different Age Groups: Cross-sectional Web-Based Study
Bibliographic record
Abstract
BACKGROUND: COVID-19 prevention behaviors have become part of our lives, and they have been reported to be associated with health literacy, knowledge, and fear. However, the COVID-19 pandemic may be characterized by different situations in each age group. Since the severity of the infection and the means of accessing information differ by age group, the relationship between health literacy, knowledge, and fear may differ. Thus, factors that promote preventive behavior may differ by age group. Clarifying the factors related to prevention behaviors by age may help us consider age-appropriate promotion. OBJECTIVE: This study aims to examine the association between COVID-19 prevention behaviors and health literacy, COVID-19 knowledge, and fear of COVID-19 by age group. METHODS: A cross-sectional study was conducted among 512 participants aged 20-69 years, recruited from a web-based sample from November 1 to November 5, 2021. A web-based self-administered questionnaire was used to obtain the participants' characteristics, COVID-19 prevention behaviors, health literacy, COVID-19 knowledge, and fear of COVID-19. The Kruskal-Wallis rank sum test was used to compare the scores of each item for each age group. The relationships among COVID-19 prevention behaviors, health literacy, COVID-19 knowledge, and fear of COVID-19 were analyzed using the Spearman rank correlation analysis. Additionally, multiple regression analysis was conducted with COVID-19 prevention behaviors as dependent variables; health literacy, COVID-19 knowledge, and fear of COVID-19 as independent variables; and sex and age as adjustment variables. RESULTS: For all participants, correlation and multiple regression analyses revealed that prevention behaviors were significantly related to health literacy, COVID-19 knowledge, and fear of COVID-19 (P<.001). Additionally, correlation analysis revealed that fear of COVID-19 was significantly negatively correlated with COVID-19 knowledge (P<.001). There was also a significant positive correlation between health literacy and COVID-19 knowledge (P<.001). Furthermore, analysis by age revealed that the factors associated with prevention behaviors differed by age group. In the age groups 20-29, 30-39, and 40-49 years, multiple factors, including health literacy, influenced COVID-19 prevention behaviors, whereas in the age groups 50-59 and 60-69 years, only fear of COVID-19 had an impact. CONCLUSIONS: The results of this study revealed that the factors associated with prevention behaviors differ by age. Age-specific approaches should be considered to prevent infection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".