Measuring psychosocial determinants of vaccination behavior in healthcare professionals: validation of the Pro-VC-Be short-form questionnaire
Bibliographic record
Abstract
BACKGROUND: Vaccine confidence among health care professionals (HCPs) is a key determinant of vaccination behaviors. We validate a short-form version of the 31-item Pro-VC-Be (Health Professionals Vaccine Confidence and Behaviors) questionnaire that measures HCPs' confidence in and commitment to vaccination. RESEARCH DESIGN AND METHODS: A cross-sectional survey among 2,696 HCPs established a long-form tool to measure 10 dimensions of psychosocial determinants of vaccination behaviors. Confirmatory factor analysis (CFA) models tested the construct validity of 69,984 combinations of items in a 10-item short form tool. The criterion validity of this tool was tested with four behavioral and attitudinal outcomes using weighted modified Poisson regressions. An immunization resource score was constructed from summing the responses of the dimensions that can influence HCPs' pro-vaccination behaviors: vaccine confidence, proactive efficacy, and trust in authorities. RESULTS: The short-form tool showed good construct validity in CFA analyses (RMSEA = 0.035 [0.024; 0.045]; CFI = 0.956; TLI = 0.918; SRMR 0.027) and comparable criterion validity to the long-form tool. The immunization resource score showed excellent criterion validity. CONCLUSIONS: The Pro-VC-Be short-form showed good construct validity and criterion validity similar to the long-form and can therefore be used to measure determinants of vaccination behaviors among HCPs.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".