Improving Influenza Vaccine Uptake During Pregnancy Through Vaccination at Point of Care: A Before-and-After Study
Bibliographic record
Abstract
OBJECTIVES: Vaccine administration where pregnant individuals receive prenatal care may increase vaccine coverage. Availability of influenza vaccine at prenatal care visits is not standard in Canada. Since the 2016-2017 influenza season, pregnant individuals can receive the influenza vaccine at the point of care (POC) in an urban clinic in Calgary, Alberta. The objective of this study was to descriptively examine vaccination rates across multiple influenza seasons for a POC vaccination in pregnancy (VIP) intervention and describe associations between influenza vaccine coverage and comorbidities and area-level socioeconomic status. METHODS: A before-and-after study design was used to examine vaccine coverage across 6 consecutive influenza seasons: 2 before (2014-2015 and 2015-2016) and 4 after POC-VIP implementation (2016-2017 to 2019-2020). We identified the birth cohort and measured influenza vaccine uptake using clinical and administrative databases. Influenza vaccination rates were computed and compared using the Fisher exact test with statistical significance at a P value of 0.05. RESULTS: A total of 4443 pregnancies were identified during the study period. The influenza vaccination rate increased in the intervention years at 40.1 per 1000 patient-weeks (P < 0.001), compared to the pre-intervention influenza seasons at 11.7 per 1000 patient-weeks. Vaccine coverage did not statistically differ between pregnancies with or without comorbidities across most seasons. Vaccine coverage decreased as material deprivation increased in pre-intervention years. CONCLUSIONS: The vaccination rate was higher in the intervention years compared to the pre-intervention period. In this study, we applied a systematic methodology to examine vaccine coverage in pregnancy and presented a descriptive examination of a POC-VIP intervention.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".