Health literacy in relation to vaccine hesitancy: Systematic review of observational studies
Bibliographic record
Abstract
Abstract Background Vaccine hesitancy is considered by the WHO as one of the major global public health threats. Identifying modifiable predictors of vaccine hesitancy is instrumental for attempts aimed at tackling this alarming phenomenon. By carrying out a systematic review, we aimed to investigate the presence and nature of the association between health literacy and vaccine hesitancy. Methods We carried out a systematic search of the literature in PubMed and Scopus, applying specific study inclusion criteria to identify studies that investigated the association of interest based on validated assessment tools. Relevant publications were screened for eligibility, while quality was independently assessed by two researchers based on the Newcastle-Ottawa scale. Results We identified 17 relevant studies (16 cross-sectional and 1 case-control). Sample size ranged from 230 to 3360, while studies involved childhood as well as adult (e.g. flu, COVID-19) vaccines. All included studies assessed health literacy and vaccine hesitancy as composite scales, with the majority using comprehensive validated scales. The majority of studies, but not all, adjusted for potential confounders. The vast majority (16/17) of studies reveal a negative association, with individuals scoring high on health literacy, scoring low on vaccine hesitancy. An exception was identified where higher health literacy was associated with higher vaccine hesitancy among parents as regards childhood vaccinations. Conclusions Our systematic review provides strong evidence suggesting that higher health literacy is linked to lower vaccine hesitancy. This inverse association might not be apparent in all contexts however, as cultural factors and the nature of vaccination, might reverse this relationship. Public Health approaches aimed at tackling vaccine hesitancy and reducing the burden from emerging and re-emerging infectious diseases, should focus on increasing health literacy, among other factors. Key messages • Overwhelming evidence suggests that higher health literacy is linked to lower vaccine hesitancy in the majority of contexts. • Increasing health literacy is a promising target for tackling vaccine hesitancy and its detrimental consequences in the fight against the control of emerging and re-emerging infectious disease.
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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 | 0.015 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".