Factors that influence vaccination communication during pregnancy: provider and patient perspectives using the theoretical domains framework
Bibliographic record
Abstract
INTRODUCTION: Vaccination during pregnancy is recommended but uptake is low and evidence on the topic is limited. AIMS: This study aimed to identify the drivers of current behavior and barriers to change for health care practitioners (HCPs) and pregnant patients in Canada. METHODS: This study is an in-depth qualitative investigation of the factors influencing HCPs' vaccination communication during pregnancy, as well as factors influencing pregnant patients' vaccination uptake in Canada using the Theoretical Domains Framework. Three data sources were used: (1) perinatal HCP interviews before COVID-19; (2) perinatal HCP interviews regarding vaccine communication after COVID-19; and (3) survey of pregnant or lactating women after COVID-19. RESULTS: Forty-seven interviews and 169 participant responses were included. Perinatal HCPs reported limited information on vaccine communication or difficulty keeping up-to-date ( Environmental context and resources ; Knowledge; Beliefs about capabilities ). HCPs lacked confidence and struggled with lack of training to address vaccine hesitancy without alienating patients ( Beliefs about capabilities; Skills ). Pregnant or lactating women struggled with the amount of information they felt was imposed on them, had concerns about the perceived negative consequences of vaccination, and felt pressure to understand what was best for them and their babies ( Knowledge; Beliefs about consequences; Social influences ). CONCLUSIONS: Our study provides a theory-based approach to identify influencing factors that can be mapped to theory-based intervention components, improving the likelihood of intervention effectiveness. The study is the first step in adapting an existing intervention to improve vaccine communication during pregnancy, ultimately, increasing vaccination uptake. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A260.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".