Understanding Influenza Vaccination During Pregnancy in Canada: Attitudes, Norms, Intentions, and Vaccine Uptake
Bibliographic record
Abstract
To improve uptake of influenza vaccine in pregnancy, it is important to understand the factors that predict prenatal vaccination. The aim of this study was to test the capability of the theory of planned behavior, augmented with information constructs, to predict and explain influenza vaccination uptake in a sample of 600 pregnant individuals in Canada. A baseline survey at the start of influenza season assessed beliefs, norms, perceived control, and information-seeking behavior related to influenza vaccination in pregnancy, as well as respondent demographics. A follow-up survey at the conclusion of influenza season assessed self-reported influenza vaccine uptake as well as infant vaccination intentions. Multivariable analysis indicated that attitudes toward influenza vaccination in pregnancy, subjective norms, information seeking, and past vaccination behavior predicted intentions to be vaccinated, and intentions predicted vaccine uptake. Neither perceived control nor demographics were significant predictors of intentions or vaccine uptake. These findings suggest that presumptive offering of vaccination in pregnancy by health care providers, as well as patient and public health educational interventions, may be effective in communicating norms and strengthening positive attitudes and intentions concerning influenza vaccination in pregnancy, resulting in higher vaccine coverage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".