The relationship between influenza vaccine hesitancy and vaccine literacy among youth and adults in China
Bibliographic record
Abstract
Objectives The present study aimed to assess influenza vaccine hesitancy and vaccine literacy levels among youth and adults in China, as well as the association between them. Methods An online cross-sectional survey was conducted in Mainland China. Participants’ total vaccine literacy and three sub-dimension vaccine literacy (knowledge literacy, competence literacy, and decision-making literacy) were assessed by a validated vaccine literacy scale. Having received influenza vaccination in the past three years or intending to accept it in next influenza season indicates less influenza vaccine hesitancy. Results Among 997 participants, a sub-optimal vaccine literacy was observed, with a mean score of 66.83 ± 10.27. Regression models 1–4 revealed that participants with middle (aOR: 1.431, P=0.039, 95% CI: 1.018~2.010) or high (aOR: 1.651, P=0.006, 95% CI: 1.157~2.354) total vaccine literacy, as well as those with high competence literacy (aOR: 1.533, P=0.017, 95% CI: 1.079~2.180), or high decision-making literacy (aOR: 1.822, P=0.001, 95% CI: 1.261~2.632) were more likely to have been vaccinated against influenza at least once in past three years. However, those with a high knowledge literacy were associated with a lower influenza vaccine rate (aOR: 0.676, P=0.046, 95% CI: 0.460~0.994). Regression models 5–8 revealed that participants with middle (aOR: 1.661, P=0.008, 95% CI: 1.142~2.414) or high total vaccine literacy (aOR: 2.645, P=0.000, 95% CI: 1.774~3.942), as well as those with middle (aOR: 1.703, P=0.005, 95% CI: 1.177~2.464) or high competence literacy (aOR: 2.346, P=0.000, 95% CI: 1.159~3.461), or high decision-making literacy (aOR: 2.294, P=0.000, 95% CI: 1.531~3.436) were more likely to express the willingness to receive the influenza vaccine in the next influenza season. Conclusion The participants’ influenza vaccine hesitancy was negatively associated with their total vaccine literacy levels and two of the three sub-dimensions: competence literacy and decision-making literacy. Knowledge literacy suggested a positive or no relationship with influenza vaccine hesitancy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".