Underlying factors impacting vaccine hesitancy in high income countries: a review of qualitative studies
Bibliographic record
Abstract
INTRODUCTION: While the scientific consensus on the benefits of vaccination is unambiguous, there is a growing proportion of the population that is skeptical about vaccination. The idea that vaccination programs are losing their momentum concerns public health agencies throughout the world. Many studies assessing determinants of vaccine acceptance have been published in the last decade. AREAS COVERED: In this article, we review the existing qualitative literature on parents' attitudes toward childhood vaccination. Studies were included if they: (1) focused on the views, decision-making, or experiences of caregivers (hereafter, referred to as 'parents') regarding vaccinations for young children; (2) used qualitative methods for both data and data analysis; (3) were conducted in countries that ranked 'very high' on the 2016 United Nations Human Development Index; and (4) had been peer-reviewed. Twenty-two (22) studies met our inclusion criteria and were reviewed, using the socio-ecological model as a conceptual framework. EXPERT COMMENTARY: Parental vaccination decisions are complex and multi-dimensional. Experiences, emotions, routine ways of thinking, information sources, peers/family, risk perceptions, and trust, among other factors, inform parents' attitudes and decision-making processes. Further research is needed in order to design evidence-informed responses to vaccine hesitancy appropriate to the setting, context, and hesitant subgroups.
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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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".