University students’ predictors of vaccine intention toward routine vaccination: a systematic review
Bibliographic record
Abstract
Abstract Introduction Vaccine hesitancy is a major challenge for national immunization programs, especially after the COVID-19 pandemic, which disrupted routine coverage for many vaccinations. Therefore, we systematically examined the determinants of vaccination intention among university students, an age group undergoing training, and for this reason particularly receptive to educational campaigns. Methods We searched PubMed, Scopus, and Web of Science. Studies were considered eligible if they analyzed vaccination intention(s) (i.e., sure/willing vs. unsure/unwilling) through multivariable logistic regression analyses in university students. All routine vaccines were considered. Study quality was assessed using the Newcastle-Ottawa scale. Determinants were grouped into three categories: contextual, individual and group, and vaccine/vaccination-specific influences. A narrative synthesis of the results was performed. Results A total of 14.940 articles were retrieved. Of these, 40 were included, 34 regarded the Human Papillomavirus (HPV) vaccine, and 6 on influenza (flu) vaccine. Out of the 282 factors found, 231 regarded the HPV vaccine. Among the contextual influences, age, gender, and sexual behavior were the most studied, whereas “HPV knowledge” (N = 44) and “awareness” (N = 16) were the most investigated among individual and group influences, showing also higher rates of statistical significance with the outcome. Lastly, among vaccine-specific influences, “healthcare professionals’ recommendation” was the most studied. As for the flu vaccine, 51 factors were found, with ‘Previous flu vaccination’ (N = 5) being the most investigated and always positively associated with vaccine intention. Conclusions Our study highlights that among university students HPV knowledge and awareness, and previous flu vaccination are the main predictors of HPV and flu vaccine intention, respectively. These results should be used in planning educational campaigns to increase vaccine uptake in this subgroup. Key messages • Vaccine Hesitancy (VH) is a major public health issue and a challenge for national immunization programs. • Our study highlights that among university students HPV knowledge and awareness, and previous flu vaccination are the main predictors of HPV and flu vaccine intention, respectively.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".