Pertussis Non-Vaccination During Pregnancy Despite Advice From Prenatal Care Providers
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to measure the proportion of non-vaccination for pertussis in mothers in Canada who had been advised by their prenatal care provider to get vaccinated, and to identify sociodemographic factors and beliefs associated with non-vaccination. METHODS: The Survey on Vaccination during Pregnancy (part of childhood National Immunization Coverage Survey) included biological mothers of children born from September 2018 to March 2019. This analysis was restricted to 2657 mothers who had been advised by their prenatal care provider to get vaccinated against pertussis during pregnancy and knew whether or not they had been vaccinated. RESULTS: Of those who had been advised to get vaccinated against pertussis, 21% were not. This rate varied across provinces and territories, ranging from 9% in Prince Edward Island to 32% in Newfoundland and Labrador. Factors independently associated with pertussis non-vaccination included lower household income, having had past live births, and having received prenatal care from an obstetrician-gynecologist or a midwife compared to a family doctor. The risk of pertussis non-vaccination despite prenatal care advice was higher for those who disagreed that the baby would be at greater risk of pertussis if the mother did not get vaccinated. It was also higher for those who disagreed with statements regarding perceived benefits of vaccination. Conversely, disagreement with statements on perceived barriers was negatively associated with pertussis non-vaccination. CONCLUSION: These findings highlight the underlying factors associated with non-vaccination against pertussis despite prenatal care provider recommendation. Some inaccurate beliefs about pertussis and vaccination during pregnancy persist, leading to non-vaccination.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".