“I try to take all the time needed, even if i do not have it!”: Knowledge, attitudes, practices of perinatal care providers in canada about vaccination
Bibliographic record
Abstract
Objective: Successful clinical conversations about vaccination in pregnancy (pertussis, COVID-19, and influenza) are key to improving low uptake rates of both vaccination in pregnancy and infancy. The purpose of this study was to understand Canadian perinatal care providers' knowledge, attitudes, and practices around vaccination in pregnancy. Methods: Qualitative interviews with 49 perinatal care providers (nurse practitioner, general practitioner, registered nurse, registered midwife, obstetrician-gynecologist, and family physicians) in 6 of 13 provinces and territories were deductively coded using directed content analysis [1] and analyzed according to key themes. Results: Participants detailed their professional training and experiences, patient community demographics, knowledge of vaccines, views and beliefs about vaccination in pregnancy, and attitudes about vaccine counselling. Providers generally described having a good range of information sources to keep vaccine knowledge up to date. Some providers lacked the necessary logistical setups to administer vaccines within their practice. Responses suggest diverging approaches to vaccine counselling. With merely hesitant patients, some opted to dig in and have more in-depth discussions, while others felt the likelihood of persuading an outright vaccine-refusing patient to vaccinate was too low to be worthwhile. Conclusion: Provider knowledge, attitudes, and practices around vaccination varied by professional background. To support perinatal providers' knowledge and practices, clinical guidelines should detail the importance of vaccination relative to other care priorities, emphasize the positive impact of engaging hesitant patients in vaccine counselling.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".