The perceived vulnerability to disease scale: Cross‐cultural measurement invariance and associations with fear of COVID‐19 across 16 countries
Bibliographic record
Abstract
Abstract Using cross‐sectional data from N = 4274 young adults across 16 countries during the COVID‐19 pandemic, we examined the cross‐cultural measurement invariance of the perceived vulnerability to disease (PVD) scale and tested the hypothesis that the association between PVD and fear of COVID‐19 is stronger under high disease threat [that is, absence of COVID‐19 vaccination, living in a country with lower Human Development Index (HDI) or higher COVID‐19 mortality]. Results supported a bi‐factor Exploratory Structural Equation Modeling model where items loaded on a global PVD factor, and on the sub‐factors of Perceived Infectability and Germ Aversion . However, cross‐national invariance could only be obtained on the configural level with a reduced version of the PVD scale (PVD‐r), suggesting that the concept of PVD may vary across nations. Moreover, higher PVD‐r was consistently associated with greater fear of COVID‐19 across all levels of disease threat, but this association was especially pronounced among individuals with a COVID‐19 vaccine, and in contexts where COVID‐19 mortality was high. The present research brought clarity into the dimensionality of the PVD measure, discussed its suitability and limitations for cross‐cultural research, and highlighted the pandemic‐related conditions under which higher PVD is most likely to go along with psychologically maladaptive outcomes, such as fear of COVID‐19.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".