Influenza vaccination in pregnancy: vaccine uptake, maternal and healthcare providers’ knowledge and attitudes. A quantitative study
Bibliographic record
Abstract
Background Influenza during pregnancy is a potentially life threatening illness. There are limited data on influenza vaccination uptake and determinants of uptake in Irish obstetric populations. Aim To determine the uptake of influenza vaccination during pregnancy; determinants of vaccination uptake; knowledge, attitudes, and concerns of postnatal women; and knowledge and attitudes of healthcare professionals (HCPs) surrounding vaccination. Design & setting A quantitative study of postnatal women attending the Rotunda Hospital, a tertiary referral maternity hospital in Dublin, Ireland. A separate quantitative study conducted by the North Dublin City GP Training Programme surveyed GPs, pharmacists, and Rotunda Hospital clinical staff. Method A paper-based survey was distributed to postnatal women. HCPs completed the survey via the online tool Survey Monkey. Results 330 patient surveys were disseminated, with a 60.0% response rate. Of 198 responders, 109 (55.1%) were vaccinated against influenza. Non-professionals were less likely to be vaccinated (adjusted odds ratio [aOR] 0.29, 95% confidence interval [CI] = 0.09 to 0.89). Vaccination in previous pregnancy (aOR 5.2, 95% CI = 1.69 to 15.62) and information from an HCP were strongly associated with vaccination (aOR 12.8, 95% CI = 2.65 to 62.5). There was a 20.2% (n = 1180) response rate among HCPs. More GPs felt that it was their role to discuss vaccination (92.9%; n = 676), and offer to vaccinate women (91.7%; n = 666) than any other HCP. Conclusion Provision of information about the importance of vaccination against influenza and pertussis during pregnancy by HCPs and their consistent recommendations in support of vaccination were key determinants of vaccine uptake during pregnancy. The sociodemographic determinants of a woman’s vaccination status should be addressed in health promotion campaigns. Education of HCPs may address knowledge gaps surrounding 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".