Educating children and adolescents about vaccines: a review of current literature
Bibliographic record
Abstract
INTRODUCTION: Until recently, research on vaccine hesitancy has focused primarily on parent populations. Although adolescent knowledge and views are gaining momentum within the literature, particularly with regards to the human papillomavirus and influenza, children remain a virtually unstudied population with regards to vaccine hesitancy. AREAS COVERED: This review focuses on the lack of literature in this area and argues for more vaccine hesitancy research involving child and adolescent populations. It also outlines special issues to consider when framing health promotion messages for children and adolescents. Finally, we explore the use of new and existing technologies as delivery mechanisms for education on vaccines and immunizations in populations of children and adolescents. EXPERT COMMENTARY: Children undergo cognitive development and experiences with vaccines (e.g. pain or education) have the potential to create future attitudes toward vaccines. This can influence future vaccine behaviour, including their participation in decision-making around adolescent vaccines, their decisions to vaccinate themselves when they are adults, and their decisions to vaccinate their own children. Interventions aimed at children, such as education, can create positive attitudes toward vaccines. These can also potentially influence parental attitudes toward vaccines as children convey this knowledge to them. Both of these impacts require further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".