Knowledge gaps and research priorities regarding vaccination in pregnancy: A Canadian perspective from the prevention of infections in the maternal-infant dyad (PRIMED) consortium
Bibliographic record
Abstract
Vaccination in pregnancy is a safe and effective method for protecting both the pregnant woman/person and their infant from communicable diseases. Despite a growing body of evidence of the benefits of vaccination during pregnancy and the widespread introduction of vaccines during pregnancy for influenza, pertussis, and COVID-19, there is no national research agenda in Canada to guide funding and research. We sought to create a roadmap for Canadian vaccination in pregnancy research. During 2017-2023, working with immunization stakeholders using in-person and virtual workshops and online surveys, we identified priorities for Canadian research on vaccination in pregnancy. Stage one (2017-18) included a workshop, followed by a survey, regarding 39 pertussis-related research gaps and priorities, and included researchers, immunization decision-makers, frontline immunizers, policymakers, and research funders. Stage two (2022) was a cross-sectional survey across eight diseases and seven research approaches sent to a similar group. During stage three (2023), we held three expert workshops focusing on three priority areas identified during stage two, including cytomegalovirus (CMV), group B streptococcus (GBS), and respiratory syncytial virus (RSV). Our work identified several research priorities, including SARS-CoV-2, CMV, GBS, and RSV vaccine trials and immunologic studies, and the need for research into the attitudes of pregnant women and other pregnant people, prenatal care providers, and frontline immunization providers toward vaccination during pregnancy. As such, we recommend focusing vaccination in pregnancy research toward these high-impact areas. These results define a roadmap for a cohesive research strategy around vaccination in pregnancy.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".