Resources available for parent-provider vaccine communication in pregnancy in Canada: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Vaccination in pregnancy (VIP) is a protective measure for pregnant individuals and their babies. Healthcare provider's (HCP) recommendations are important in promoting VIP. However, a lack of strong recommendations and accessible resources to facilitate communication impact uptake. This study sought to determine the extent of and characterise the resources available for parent-provider vaccine communication in pregnancy in Canada using a behavioural theory-informed approach. DESIGN: Scoping review. METHODS: In accordance with the JBI methodology, nine disciplinary and interdisciplinary databases were searched, and a systematic grey literature search was conducted in March and January 2022, respectively. Eligible studies included resources available to HCPs practising in Canada when discussing VIP, and resources tailored to pregnant individuals. Two reviewers piloted a representative sample of published and grey literature using inclusion-exclusion criteria and the Authority, Accuracy, Coverage, Objectivity, Date, Significance guidelines (for grey literature only). Sixty-five published articles and 1079 grey reports were screened for eligibility, of which 19 articles and 166 reports were included, respectively. RESULTS: From the 19 published literature articles and 166 grey literature reports, 95% were driven by the 'Knowledge' domain of the Theoretical Domains Framework, while n=34 (18%) addressed the 'Skills' domain. Other gaps included a lack of VIP-specific tools to address hesitancy and a lack of information on culturally safe counselling practices. CONCLUSION: The study suggests a need for resources in Canada to improve VIP communication skills and improve access to vaccination information for HCPs and pregnant individuals. The absence of such resources may hinder VIP uptake.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.022 | 0.037 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".