The COVID-19 Preventive Behaviors Index: Development and Validation in Two Samples From the United Kingdom
Bibliographic record
Abstract
Monitoring compliance with, and understanding the factors affecting, COVID-19 preventive behaviors requires a robust index of the level of subjective likelihood that the individual will engage in key COVID-19 preventive behaviors. In this article, the psychometric properties of the COVID-19 Preventive Behaviors Index (CPBI), including its development and validation in two samples in the United Kingdom, are described. Exploratory and confirmatory factor analyses were performed on data from 470 participants in the United Kingdom who provided demographic information and completed the Fear of COVID-19 Scale, the COVID-19 Own Risk Appraisal Scale (CORAS) and the CPBI. Results showed that a unidimensional, 10-item model fits the data well, with satisfactory fit indices, internal consistency and high item loadings onto the factor. The CPBI correlated positively with both fear and perceived risk of COVID-19, suggesting good concurrent validity. The CPBI is a measure of the likelihood of engaging in preventive activity, rather than one of intention or actual action. It is adaptable enough to be used over time as a monitoring instrument by policy makers and a modeling tool by researchers.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".