International adaptation and validation of the Pro-VC-Be: measuring the psychosocial determinants of vaccine confidence in healthcare professionals in European countries
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals (HCPs) play an important role in vaccination; those with low confidence in vaccines are less likely to recommend them to their patients and to be vaccinated themselves. The study's purpose was to adapt and validate long- and short-form versions of the International Professionals' Vaccine Confidence and Behaviors (I-Pro-VC-Be) questionnaire to measure psychosocial determinants of HCPs' vaccine confidence and their associations with vaccination behaviors in European countries. RESEARCH DESIGN AND METHODS: After the original French-language Pro-VC-Be was culturally adapted and translated, HCPs involved in vaccination (mainly GPs and pediatricians) across Germany, Finland, France, and Portugal completed a cross-sectional online survey in 2022. A 10-factor multigroup confirmatory factor analysis (MG-CFA) of the long-form (10 factors comprising 34 items) tested for measurement invariance across countries. Modified multiple Poisson regressions tested the criterion validity of both versions. RESULTS: 2,748 HCPs participated. The 10-factor structure fit was acceptable to good everywhere. The final MG-CFA model confirmed strong factorial invariance and showed very good fit. The long- and short-form I-Pro-VC-Be had good criterion validity with vaccination behaviors. CONCLUSION: This study validates the I-Pro-VC-Be among HCPs in four European countries; including long- and short-form tools for use in research and public health.
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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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".