A scoping review on the decision-making dynamics for accepting or refusing the COVID-19 vaccination among adolescent and youth populations
Bibliographic record
Abstract
BACKGROUND: Global COVID-19 vaccinations rates among youth and adolescent populations prove that there is an opportunity to influence the acceptance for those who are unvaccinated and who are hesitant to receive additional doses. This study aimed to discover the acceptance and hesitancy reasons for choosing or refusing to be vaccinated against COVID-19. METHODS: A scoping review was conducted, and articles from three online databases, PubMed, Wiley, and Cochrane Library, were extracted and screened based on exclusion and PICOs criteria. A total of 21 studies were included in this review. Data highlighting study attributes, characteristics, and decision-making dynamics were extracted from the 21 studies and put into table format. RESULTS: The results showed that the primary drivers for accepting the COVID-19 vaccine include protecting oneself and close family/friends, fear of infection, professional recommendations, and employer obligations. Primary hesitancy factors include concerns about safety and side effects, effectiveness and efficacy, lack of trust in pharmaceuticals and government, conspiracies, and perceiving natural immunity as an alternative. CONCLUSIONS: This scoping review recommends that further research should be conducted with adolescent and youth populations that focus on identifying health behaviors and how they relate to vaccine policies and programs.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".