Strategies to Improve Influenza Vaccination in Pediatric Inflammatory Bowel Disease Through Education and Access
Bibliographic record
Abstract
BACKGROUND: Influenza vaccine uptake remains low in patients with inflammatory bowel disease despite an increased risk of complications from infection. We studied barriers to vaccination and evaluated the impact of an educational intervention and vaccine provision in pediatric patients with inflammatory bowel disease. METHODS: A prospective cohort study was completed over 2 successive influenza seasons. In year 1, we surveyed parents and patients aged 14 years or older regarding influenza vaccination. In year 2, before the influenza season, patients were provided with an educational module. After influenza vaccine availability, patients were offered both education and vaccine in clinic. Chi-squared analysis was used to identify significant differences in vaccination rates in each intervention group. Demographic factors were associated with vaccination status. RESULTS: In year 1, 180 parents and 183 adolescents completed the survey. In year 1, 47% of patients obtained the influenza vaccine and 34% of patients reported obtaining the vaccine annually. Top reasons for nonvaccination were perceived lack of benefit (28%) and concerns about adverse events (19%). In year 2 (n = 228), the vaccination rate in patients who received the education alone was 75.0%, compared with 89.5% of patients who received both education and vaccine access in clinic (P = 0.0043). Among the patients who took part in both study years (n = 129), influenza vaccination rates increased from 45% in year 1 to 82% in year 2 (P < 0.0001). CONCLUSIONS: The educational intervention was associated with improved influenza vaccination rates. Additional vaccine uptake was achieved with provision of influenza vaccination during clinic visits.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".