Exploring the Influencing Factors of COVID-19 Vaccination Willingness among Young Adults in China
Bibliographic record
Abstract
Vaccine uptake is considered as one of the most effective methods of defending against COVID-19 (coronavirus disease 2019). However, many young adults are hesitant regarding COVID-19 vaccines, and they actually play an important role in virus transmission. Based on a multi-theory model, this study aims to explore the influencing factors related to COVID-19 vaccine willingness among young adults in China. Using semi-structured interviews, this study explored the factors that would motivate young adults with vaccine hesitancy to get the COVID-19 vaccine. Thematic analysis was used to analyze the interview data with topic modeling as a complementarity method. After comparing the differences and similarities of results generated by thematic analysis and topic modeling, this study ultimately identified ten key factors related to COVID-19 vaccination intention, including the effectiveness and safety of vaccines, application range of vaccine, etc. This study combined thematic analysis with machine learning and provided a comprehensive and nuanced picture of facilitating factors for COVID-19 vaccine uptake among Chinese young adults. Results may be taken as potential themes for authorities and public health workers in vaccination campaigns.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".