Developing product label information to support evidence-informed use of vaccines in pregnancy
Bibliographic record
Abstract
BACKGROUND: Product labelling information describing the use of vaccines in pregnancy continues to contain cautionary language even after clinical and epidemiological evidence of safety becomes available. This language raises safety concerns among healthcare providers who may hesitate to recommend vaccines during pregnancy. PURPOSE: To develop clear evidence-based language about vaccine safety and effectiveness in pregnancy for inclusion in vaccine product labels. METHODS: We conducted a three-stage consensus-methods project with stakeholders, including: healthcare providers, vaccine regulators, industry representatives, and experts in public health, communication, law, ethics, and social sciences. Using qualitative and quantitative methods, we held a nominal group technique (NGT) meeting, followed by a Delphi survey, and then a consensus workshop with a subset of Delphi participants. We developed a methodological tool to analyse data for consensus. PRINCIPAL RESULTS: Stakeholders (N = 14) at the NGT meeting drafted product label statements for evaluation in the Delphi survey. Survey participants (N = 41) provided feedback on statements for five hypothetical vaccines. Workshop participants (N = 27) initiated discussions that demonstrated a lack of awareness that the regulatory purpose of product labels is to provide a scientific summary of product-specific pre-clinical and clinical trial data. Each stage of this project built on earlier stages until we achieved strong consensus on the language, structure, and types of data that stakeholders wanted to include in inactivated influenza vaccine (IIV) and tetanus-diphtheria-acellular pertussis (Tdap) vaccine product labels in Canada. CONCLUSIONS: The revised statements for IIV and Tdap aligned with workshop participants' goals that the product label be evidence-based, with a consistent structure and language that is easily understood by healthcare providers. Emergent methods uncovered stakeholder concerns about the regulatory purpose, content, and evidence used in product labels. Involving healthcare providers in the development and regular updating of product information could prevent interpretations of that information that contribute to vaccine hesitancy.
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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 | 0.319 | 0.371 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".