Community Engagement in Vaccination Promotion: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Community engagement plays a vital role in global immunization strategies, offering the potential to overcome vaccination hesitancy and enhance vaccination confidence. Although there is significant backing for community engagement in health promotion, the evidence supporting its effectiveness in vaccination promotion is fragmented and of uncertain quality. Objective This review aims to systematically examine the effectiveness of different contents and extent of community engagement for promoting vaccination rates. Methods This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive and exhaustive literature search was performed in 4 English databases (PubMed, Embase, Web of Science, and Cochrane Library) and 2 Chinese databases (CNKI and Wan Fang) to identify all possible articles. Original research articles applying an experimental study design that investigated the effectiveness of community engagement in vaccination promotion were eligible for inclusion. Two reviewers independently performed the literature search, study selection, quality assessment, and data extraction. Discrepancies were resolved through discussion, with the arbitration of a third reviewer where necessary. Results A total of 20 articles out of 11,404 records from 2006 to 2021 were retrieved. The studies used various designs: 12 applied single-group pre-post study designs, 5 were cluster randomized controlled trials (RCTs), and 3 were non-RCTs. These studies targeted multiple vaccines, with 8 focusing on children’s immunization, 8 on human papillomavirus vaccine, 3 on hepatitis B virus vaccine, and 1 on COVID-19 vaccine. The meta-analysis revealed significant increases in vaccination rates both in pre-post comparison (rate difference [RD] 0.34, 95% CI 0.21-0.47, I2=99.9%, P<.001) and between-group comparison (RD 0.18, 95% CI 0.07-0.29, I2=98.4%, P<.001). The meta-analysis revealed that participant recruitment had the largest effect size (RD 0.51, 95% CI 0.36-0.67, I2=99.9%, P<.001), followed by intervention development (RD 0.36, 95% CI 0.23-0.50, I2=100.0%, P<.001), intervention implementation (RD 0.35, 95% CI 0.22-0.47, I2=99.8%, P<.001), and data collection (RD 0.34, 95% CI 0.19-0.50, I2=99.8%, P<.001). The meta-analysis indicated that high community engagement extent yielded the largest effect size (RD 0.49, 95% CI 0.17-0.82, I2=100.0%, P<.001), followed by moderate community engagement extent (RD 0.45, 95% CI 0.33-0.58, I2=99.6%, P<.001) and low community engagement extent (RD 0.15, 95% CI 0.05-0.25, I2=99.2%, P<.001). The meta-analysis revealed that “health service support” demonstrated the largest effect sizes (RD 0.45, 95% CI 0.25-0.65, I2=99.9%, P<.001), followed by “health education and discussion” (RD 0.39, 95% CI 0.20-0.58, I2=99.7%, P<.001), “follow-up and reminder” (RD 0.33, 95% CI 0.23-0.42, I2=99.3%, P<.001), and “social marketing campaigns and community mobilization” (RD 0.24, 95% CI 0.06-0.41, I2=99.9%, P<.001). Conclusions The results of this meta-analysis supported the effectiveness of community engagement in vaccination promotion with variations in terms of engagement contents and extent. Community engagement required a “fit-for-purpose” approach rather than a “one-size-fits-all” approach to maximize the effectiveness of vaccine promotion. Trial Registration PROSPERO CRD42022339081; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=339081
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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 | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".