A cross-sectional study assessing Pro-VC-Be short-form questionnaire in Canada; measuring psychosocial determinants of vaccination behavior in Canadian healthcare professionals
Bibliographic record
Abstract
Vaccine hesitancy poses a significant challenge to worldwide public health and has been exacerbated by the COVID-19 pandemic, leading to heightened polarization and the spread of misinformation. Addressing vaccine hesitancy requires multifaceted strategies in which healthcare professionals (HCPs) play a critical role. Nonetheless, HCPs may also be hesitant toward vaccination. The 31-item original Pro-VC-Be tool, designed to measure the psychosocial determinants of vaccine attitudes in HCPs, was first validated in France, French-speaking Belgian regions, and Quebec (Canada). The validity of a short-form version was evaluated and found to be comparable to that of the long-form. Given differing vaccination recommendations and the changing pandemic context, assessing the tool’s stability among diverse Canadian HCPs is crucial. Relying on the original short version of the Pro-VC-Be tool, a cross-sectional online survey was conducted among various Canadian HCPs (N = 544) to explore the psychosocial determinants that impact vaccination-related behaviors (frequency of general vaccination activity, vaccine recommendations activity, and willingness to recommend vaccines). The findings underscore three crucial dimensions – vaccine confidence, proactive efficacy, and trust in authorities – as robust predictors of positive professional practice and attitudes, and thus globally support the results obtained in previous studies using the Pro-VC-Be tool. HCPs with higher vaccine confidence, high proactive efficacy, and higher trust in authorities were 80% and 180% more likely to recommend vaccines to their patients and 80% more likely to have received a COVID-19 vaccine than other HCPs, respectively. By identifying the root causes of vaccine hesitancy among HCPs, adapted strategies can be developed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".