A Moderated Mediation Model Explaining the Relationship Between Risk-Group Membership, Threat Perception, Knowledge, and Adherence to COVID-19 Behavioral Measures
Bibliographic record
Abstract
Background: COVID-19 is a threat to individual and global health, thus, reducing the disease's spread is of significant importance. However, adherence to behavioral measures against the spread of COVID-19 is not universal, even within vulnerable populations who are at higher risk of exposure to the virus or severe COVID-19 infection. Therefore, this study investigates how risk-group membership relates to adherence to COVID-19 behavioral measures, whether perceived threat of COVID-19 is a mechanism explaining this relationship, and whether knowledge about COVID-19 moderates these effects. Methods: = 4,096) representative of the adult population in Germany with regard to gender, age (18 to 74), and province. Therein, we assessed risk group membership with two indicators (risk of exposure to COVID-19 and risk of severe COVID-19 infection), perceived COVID-19 threat with the Perceived Coronavirus Threat Questionnaire, knowledge about COVID-19 with a knowledge test; and adherence to six behavioral measures to protect against the spread of COVID-19 (e.g., keeping distance, using mouth-nose protection, and following contact restrictions). We used moderated mediation models to test whether perceived threat mediates the relationship between risk-group membership and adherence and whether knowledge about COVID-19 moderates this relationship. Results: We found that risk group members had more perceived COVID-19 threat and that knowledge about COVID-19 increased perceived threat. Moreover, risk group membership had a positive direct effect on adherence to most behavioral measures and risk group members with less knowledge about COVID-19 violated measures more frequently. Risk-group membership also had positive indirect effects on adherence via perceived COVID-19 threat. The moderated indirect effects of threat indicate that threat led to more adherence when knowledge was low, but lost relevance as knowledge increased. Conclusion: The results may help to evaluate disease-regulation measures and to combat the pandemic more effectively. For example, increasing COVID-19 knowledge in the general population could increase adherence to COVID-19 behavioral measures. However, policy makers should be mindful that this could also have negative mental health implications as knowledge increases perceived COVID-19 threat.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".