Vaccine hesitancy among hospital staff physicians: A cross-sectional survey in France in 2019
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals, because they recommend vaccines to their patients, answer their questions, and vaccinate them, are the cornerstone of vaccination in France. They can nonetheless be affected by vaccine hesitancy (VH). AIMS: We sought to study the opinions, practices, and perceptions of French hospital staff physicians (HSPs) toward vaccination and the prevalence and correlates of VH among them. METHODS: We conducted a cross-sectional survey in 14 public hospitals in France from September 2018 to October 2019. HSPs completed a standardized questionnaire -most of the time face-to-face - about their vaccine-related attitudes and practices. Data were weighted for age and sex. An agglomerative hierarchical cluster analysis of the HSPs' perceptions and opinions toward vaccination allowed us to identify vaccine-hesitant HSPs, and multiple Poisson regression with robust standard errors let us study the factors associated with VH. RESULTS: The study included 1,795 HSPs (participation rate: 86%). Almost all (93.7%) were strongly favorable to vaccination, even though 42.2% (95CI = 39.8-44.6) showed moderate VH. VH prevalence was lowest among infectious disease specialists (12.3%; 95CI = 6.7-21.3) and pediatricians (27.7%; 95CI = 21.4-35.2). Hesitant HSPs were less trustful of vaccination information sources and doubted the safety of vaccines more often than HSPs with almost no VH. Compared with non-hesitant HSPs, those with higher VH had less often taken a medical course about vaccination and were less likely to be vaccinated against seasonal influenza, to recommend vaccines to their patients and to try to convince vaccine-hesitant patients to be vaccinated. CONCLUSIONS: Strong favorability to vaccination does not prevent VH, which was observed in most specialties. Interventions are required to help hesitant HSPs to adopt more proactive vaccination practices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".