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 (<i>N</i> = 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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".