Fear of COVID-19 and Prevention Behaviors: Cross-Lagged Panel Analysis
Bibliographic record
Abstract
BACKGROUND: The ongoing COVID-19 pandemic has brought forth conversations about effective behavior change models for increasing prevention behavior, ranging from wearing masks in public to physical distancing. Among the considered behavior change techniques is the use of fear appeals, through which a negative possible outcome is emphasized to invoke fear, which in turn may promote prevention behaviors to counter the likelihood of the negative outcome. Although fear is hypothesized as health promoting in some theories of health behavior, little research has rigorously assessed the relationship. OBJECTIVE: In our exploratory analyses, we aim to examine the association, including directionality of the association between fear of COVID-19 and COVID-19 prevention behaviors across 2 time points during the early COVID-19 pandemic among a sample of US women. METHODS: The COPE study, a web-based survey of US women's COVID-19 experiences, was deployed in May-June 2020 (time 1) with follow-up in December 2020-January 2021 (time 2; n=200). Demographic characteristics as well as fear of COVID-19 and COVID-19 prevention behaviors (eg, staying home except for essential activities, physical distancing in public, and masking in public) were measured. Descriptive and bivariate analyses were used to characterize COVID-19 prevention behaviors and fear of COVID-19 among participants. Cross-lagged panel analysis, a type of structural equation modeling that assesses directionality of temporal associations, was used to understand relationships, if any, between variables of interest. RESULTS: We found cross-sectional associations between fear of COVID-19 and staying home and physical distancing, as well as temporal associations between fear at time 1 and time 2 and prevention behaviors at time 1 and time 2. However, results of the cross-lagged panel analysis indicated no cross-lagged temporal relationships between fear of COVID-19 and COVID-19 prevention behaviors 6 months apart. CONCLUSIONS: Fear of COVID-19 did not appear to predict COVID-19 prevention behaviors 6 months after initial measurements among the sample of women recruited for our study. Future research should rigorously test these associations longitudinally, and alternative methods of public health prevention promotion should be considered.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".