Effectiveness of hospital-based strategies for improving childhood immunization coverage: A systematic review
Bibliographic record
Abstract
BACKGROUND: Hospital settings represent an opportunity to offer and/or promote childhood vaccination. The purpose of the systematic review was to assess the effectiveness of different hospital-based strategies for improving childhood vaccination coverage. METHODS: A systematic search of multiple bibliographic databases, thesis databases, and relevant websites was conducted to identify peer-reviewed articles published up to September 20, 2021. Articles were included if they evaluated the impact of a hospital (inpatient or emergency department)-based intervention on childhood vaccination coverage, were published in English or French, and were conducted in high-income countries. High quality studies were included in a narrative synthesis. RESULTS: We included 25 high quality studies out of 7,845 unique citations. Studies focused on routine, outbreak, and influenza vaccines, and interventions included opportunistic vaccination (i.e. vaccination during hospital visit) (n = 7), patient education (n = 2), community connection (n = 2), patient reminders (n = 2), and opportunistic vaccination combined with patient education and/or reminders (n = 12). Opportunistic vaccination interventions were generally successful at improving vaccine coverage, though results ranged from no impact to vaccinating 71 % of eligible children with routine vaccines and 9-61 % of eligible children with influenza vaccines. Interventions that aimed to increase vaccination after hospital discharge (community connection, patient education, reminders) were less successful. CONCLUSIONS: Some interventions that provide vaccination to children accessing hospitals improved vaccine coverage; however, the baseline coverage level of the population, as well as implementation strategies used impact success. There is limited evidence that interventions promoting vaccination after hospital discharge are more successful if they are tailored to the individual.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".