Factors Related to COVID-19 Preventive Behaviors: A Structural Equation Model
Bibliographic record
Abstract
Background:While COVID-19 has rapidly spread around the world, and vaccines are not widely available to the general population, the World Health Organization outlines preventive behavior as the most effective way to limit the rapid spread of the virus. Preventive behavior is associated with a number of factors that both encourage and discourage prevention. Aim:The aim of this research was to study COVID-19 threat appraisal, fear of COVID-19, trust in COVID-19 information sources, COVID-19 conspiracy beliefs and the relationship of socio-demographic variables (gender, age, level of education, place of residence, and employment status) to COVID-19 preventive behavior. Methods:The data originate from a national cross-sectional online survey (N= 2,608) undertaken in July 2020. The data were analyzed using structural equation modeling. Results:COVID-19 threat appraisal, trust in COVID-19 information sources, and fear of COVID-19 are all significant predictors of COVID-19 preventive behaviors. Together they explain 26.7% of the variance of this variable. COVID-19 conspiracy beliefs significantly negatively predict COVID-19 threat appraisal (R2= 0.206) and trust in COVID-19 information sources (R2= 0.190). COVID-19 threat appraisal contributes significantly and directly to the explanation of the fear of COVID-19 (R2= 0.134). Directly, as well as mediated by COVID-19 conspiracy beliefs, threat appraisal predicts trust in COVID-19 information sources (R2= 0.190). The relationship between COVID-19 threat appraisal and COVID-19 preventive behaviors is partially mediated by fear of COVID-19 (indirect effect 28.6%) and trust in information sources (15.8%). Socio-demographic variables add very little in prediction of COVID-19 preventive behavior. Conclusions:The study results demonstrate that COVID-19 threat appraisal is the most important factor associated with COVID-19 preventive behavior. Those Latvian residents with higher COVID-19 threat appraisal, experienced higher levels of fear of COVID-19, had more trust in COVID-19 information sources, and were more actively involved in following COVID-19 preventive behaviors. COVID-19 conspiracy beliefs negatively predict COVID-19 threat appraisal and trust in COVID-19 information sources, but not the COVID-19 preventive behaviors. Socio-demographic factors do not play an important role here.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".