An instrument to measure psychosocial determinants of health care professionals’ vaccination behavior: Validation of the Pro-VC-Be questionnaire
Bibliographic record
Abstract
OBJECTIVES: The lack of validated instruments assessing vaccine hesitancy/confidence among health care professionals (HCPs) for themselves, and their patients led us to develop and validate the Pro-VC-Be instrument to measure vaccine confidence and other psychosocial determinants of HCPs' vaccination behavior among diverse HCPs in different countries. METHODS: Cross-sectional survey in October-November 2020 among 1,249 GPs in France, 432 GPs in French-speaking parts of Belgium, and 1,055 nurses in Quebec (Canada), all participating in general population immunization. Exploratory and confirmatory factor analyses evaluated the instrument's construct validity. We used HCPs' self-reported vaccine recommendations to patients, general immunization activity, self-vaccination, and future COVID-19 vaccine acceptance to test criterion validity. RESULTS: The final results indicated a 6-factor structure with good fit: vaccine confidence (combining complacency, perceived vaccine risks, perceived benefit-risk balance, perceived collective responsibility), trust in authorities, perceived constraints, proactive efficacy (combining commitment to vaccination and self-efficacy), reluctant trust, and openness to patients. The instrument showed good convergent and criterion validity and adequate discriminant validity. CONCLUSIONS: This study found that the Pro-VC-Be is a valid instrument for measuring psychosocial determinants of HCPs' vaccination behaviors in different settings. Its validation is currently underway in Europe among various HCPs in different languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".