Attitudes and Beliefs of Pregnant Women and New Mothers Regarding Influenza Vaccination in British Columbia
Bibliographic record
Abstract
OBJECTIVE: Although pregnant women have increased risks for influenza morbidity and mortality, influenza vaccination rates among pregnant women in Canada are consistently very low. This mixed-methods study investigated the attitudes and behaviour of pregnant women and new mothers regarding seasonal and pandemic influenza vaccination. METHODS: We conducted a baseline survey and qualitative focus groups with 34 women (26 pregnant women and 8 mothers of newborns), with a follow-up survey to assess outcomes at the end of the subsequent influenza season. Data analysis included descriptive statistics and directed content analysis based on the health belief model. RESULTS: Most women did not consider influenza vaccination to be an important preventative measure to take while pregnant, although some were more willing to consider vaccination during a pandemic. Omission bias played a substantial role as justification for not vaccinating. Participants expressed confusion about recommendations regarding vaccination during pregnancy and frustration with inconsistent messages from health care providers (HCPs), particularly with regard to pandemic vaccines. Women were vaccinated when they perceived themselves and/or their babies to be at increased risk for influenza. Vaccinated women had strong normative influences (usually an HCP or a family member) that affected their decision. Intentions accurately predicted behaviour for women who did and did not intend to be vaccinated. CONCLUSION: Pregnant women did not perceive themselves to be at increased risk for influenza and did not believe that influenza vaccination was a necessary preventative health measure. A lack of safety information about vaccination during pregnancy and inconsistent messages from HCPs were barriers to vaccine acceptance. Recommendations from maternity care providers and communication about the severity of and susceptibility to influenza for pregnant women would facilitate vaccine uptake.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".