Improving rates of maternal immunization: Challenges and opportunities
Bibliographic record
Abstract
OBJECTIVES: An increasing number of vaccines are recommended or are being developed for use during pregnancy to protect women, fetuses, and/or newborns. For vaccines that are already recommended, vaccine uptake is variable and well below desired target. We reviewed the literature related to factors that affect a healthcare provider's recommendation and a woman's willingness to be vaccinated during pregnancy. DESIGN: A scoping review of published literature from 2005 to 2015 was undertaken and all relevant articles were abstracted, summarized, and organized thematically. RESULTS: Barriers and facilitators were identified that either decreased or increased the likelihood of a healthcare provider offering and a pregnant woman accepting vaccination during pregnancy. Concern about the safety of vaccines given during pregnancy was the most often cited barrier among both the public and healthcare providers. Other barriers included doubt about the effectiveness of the vaccine, lack of knowledge about the burden of disease, and not feeling oneself to be at risk of the infection. Major facilitators for maternal immunization included specific safety information about the vaccine in pregnant women, strong national recommendations, and healthcare providers who both recommended and provided the vaccine to their patients. Systems barriers such as inadequate facilities and staffing, vaccine purchase and storage, and reimbursement for vaccination were also cited. Evidence-based interventions were few, and included text messaging reminders, chart reminders, and standing orders. CONCLUSIONS: In order to have an effective vaccination program, improvements in the uptake of recommended vaccines during pregnancy are needed. A maternal immunization platform is required that normalizes vaccination practice among obstetrical care providers and is supported by basic and continuing education, communication strategy, and a broad range of research.
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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 | 0.058 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".