Vaccines are underused in pregnancy: what about knowledge, attitudes and practices of providers?
Bibliographic record
Abstract
INTRODUCTION: To investigate actual knowledge of official recommendations towards seasonal influenza (SID), and Tetanus-diphtheria acellular-pertussis (Tdap) vaccines in obstetrics/gynecologists (OBGYN). METHODS: PubMed and EMBASE databases were searched. A meta-analysis was performed to calculate odds ratio (OR) and 95% confidence interval (CI) among case controls, cross-sectional studies, either questionnaire or laboratory exams based. Results. A total of 6 studies met inclusion criteria, including 1323 OBGYN from 5 different countries. Overall, around 99% of sampled professionals were aware that official recommendations towards SID in pregnancy do exist, compared to 92% for Tdap, with significant heterogeneity (I2 > 95%, p < 0.001). Concerns about vaccine safety was reported by 10% of respondents for Tdap, and by 6.0% for SID, but again available studies were substantially heterogenous (I2 = 86.7% and 86.0%, p < 0.001). Eventually, 93% of respondents actively recommended SID in pregnancy, compared to 88% for Tdap (I2 98.8% and I2 95.9%, respectively p < 0.001). The evidence of significant publication bias was initially subjectively identified from the funnel plot, and then objectively confirmed through the regression test for all analyses. CONCLUSIONS: These results suggest an appropriated understanding of official recommendation among sampled OBGYN, with high shares of professionals actively promoting vaccination practices among their patients. Despite the high heterogeneity and the significant publication bias we identified, our results also hint towards extensive knowledge gaps of OBGYN, and particularly regarding unmotivated concerns about vaccine safety. As a consequence, appropriate information and formation campaigns should be appropriately tailored.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".