Vaccination during pregnancy: Canadian maternity care providers' opinions and practices
Bibliographic record
Abstract
A number of countries have implemented vaccination in pregnancy as a strategy to reduce the burden of influenza and pertussis. The aim of this study was to assess the involvement of Canadian maternity care providers in administration of vaccines to their pregnant patients. A cross-sectional web-based survey was sent to family physicians, obstetricians-gynecologists, midwives, pharmacists, and nurses. A multivariable logistic regression model was used to determine variables independently associated with offering vaccination services in pregnancy in providers' practice. A total of 1,135 participants participated. Overall, 64% (n = 724) of the participants reported offering vaccines in their practice and 56% (n = 632) reported offering vaccines to pregnant patients. The main reasons reported for not offering vaccination services in pregnancy were the belief that vaccination was outside of the scope of practice; logistical issues around access to vaccines; or lack of staff to administer vaccines. In multivariable analysis, the main factors associated with vaccination of pregnant patients in practices where vaccination services were offered were: providers' confidence in counseling pregnant patients about vaccines, seeing fewer than 11 pregnant patients on average each week, and being a nurse or a family physician. Although the majority of participants expressed strong support for vaccination during pregnancy, half were not offering vaccination services in their practice. Many were not equipped to offer vaccines in their practice or felt that it was not their role to do so. To enhance vaccine acceptance and uptake in pregnancy, it will be important to address the logistical barriers identified in this study.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".