Do health care providers trust product monograph information regarding use of vaccines in pregnancy? A qualitative study
Bibliographic record
Abstract
BACKGROUND: Influenza immunization is recommended in pregnancy to prevent severe infections in pregnant women and newborns, yet vaccine uptake remains low. Studies suggest that cautionary language in vaccine product monographs regarding safety and use in pregnancy affects health care providers' perceptions of vaccine safety and how they counsel pregnant women. OBJECTIVE: To conduct a qualitative analysis of health care provider perceptions of the safety of inactivated influenza vaccines and their recommendations for use in pregnancy based on product monograph language statements. METHODS: Health care providers were recruited at two international health conferences and from teaching programs in Ethiopia, Ghana, Uganda, and Laos during September and October 2015. After reading the product monograph excerpts for three licensed inactivated influenza vaccines, participants completed a ten-item online survey with quantitative and qualitative components that captured perceptions of vaccine safety. RESULTS: Health care providers identified a lack of trust in manufacturers' and product monograph information. They perceived product monograph language as ambiguous and not "up-to-date" with current evidence. Health care providers wanted product monograph language that clearly conveyed evidence for the risks and benefits of the vaccine in an understandable manner. CONCLUSION: This study suggests that adopting best practices in the wording of product monographs would help to support evidence-based use of vaccines in pregnant women.
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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 | 0.023 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".