Expected challenges of implementing universal pertussis vaccination during pregnancy in Quebec: a cross-sectional survey
Bibliographic record
Abstract
Background: Vaccination of all pregnant women with an acellular pertussis-containing vaccine (tetanus, diphtheria, pertussis [Tdap]) was recently recommended in Canada, ideally between 27 and 32 weeks of gestation. This study aimed to describe the existing model of prenatal care in Quebec and determine to what extent maternal vaccination against pertussis could be integrated into this model. Methods: In Quebec, health care is organized around Local Community Service Centres (LCSCs) that serve specific geographic areas. For each of 158 LCSCs (98.1% of LCSCs in the province), we invited 1 nurse or manager involved in prenatal care to participate in a cross-sectional Web-based survey. The structure of prenatal care visits and potential integration of maternal Tdap vaccination into the existing model were documented and compared according to urbanization level, determined with the use of census data. Results: A completed survey was obtained for 127 LCSCs (response rate 80.4%). Only 13 (10.2%) and 14 (11.0%) LCSCs offered on-site visits with a nurse for the majority of pregnant women during the second and third trimesters, respectively. A significantly higher proportion of rural LCSCs than urban LCSCs offered on-site visits to pregnant women in the third trimester (13 [18%] v. 1 [2%]) (p = 0.003). In at least 50 LCSC service areas (39.4%), vaccines were not available in most medical clinics offering prenatal care. Interpretation: Given the current situation in Quebec, implementing universal maternal Tdap vaccination may be challenging, which may result in suboptimal vaccine coverage among pregnant women. As other Canadian provinces may face similar issues, a priority will be to evaluate province-based implementation models to develop efficient ways to provide maternal Tdap vaccination across Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".