Vaccination Hesitancy and Its Impact on Immunization Coverage in Pediatrics: A Systematic Review
Bibliographic record
Abstract
One significant global health issue that is present in more than 190 nations globally is routine vaccination reluctance. This study aimed to synthesize the current evidence on vaccination hesitancy and its impact on immunization coverage in pediatrics. We searched for relevant studies across four databases (Scopus, Web of Science, PubMed/EMBASE, and Cumulated Index in Nursing and Allied Health Literature). Prespecified inclusion and exclusion criteria were used to extract relevant studies while excluding irrelevant ones. We found 4,085 studies on four different databases in which 23 satisfied the inclusion and exclusion criteria. These 23 relevant studies involving 29,131 parents, guardians, and caregivers from over 30 countries met the inclusion criteria and quality assessment. Studies were assessed for risk bias using the Newcastle-Ottawa scale. Vaccination hesitancy is caused by several factors, such as cultural customs, economic reforms, perceived rumors, myths, misconceptions, physicians and other healthcare professionals, and perceived risks and problems of vaccines. These results highlight the importance of addressing demand-side factors related to socioeconomic determinants and supply-side issues such as improving health literacy, combating misinformation, ensuring clarity in communication, and promoting a consistent, evidence-based message. More observations and research should be conducted regularly to develop strategies for encouraging youngsters to receive immunizations in large quantities.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".