Reasons for COVID-19 Vaccine Hesitancy Among Chinese People Living With HIV/AIDS: Structural Equation Modeling Analysis
Bibliographic record
Abstract
BACKGROUND: Many countries and organizations recommended people living with HIV/AIDS (PLWHA) receive the COVID-19 vaccine. However, vaccine hesitancy still exists and becomes a barrier for promoting COVID-19 vaccination among PLWHA. OBJECTIVE: This study aims to investigate factors that contributed to COVID-19 vaccine hesitancy among PLWHA. METHODS: The study used a multicenter cross-sectional design and an online survey mode. We recruited PLWHA aged 18-65 years from 5 metropolitan cities in China between January 2021 and February 2021. Participants completed an online survey through Golden Data, a widely used encrypted web-based survey platform. Multiple linear regression models were used to assess the background characteristics in relation to COVID-19 vaccine hesitancy, and structural equation modeling was performed to assess the relationships among perceived benefits, perceived risks, self-efficacy, subjective norms, and COVID-19 vaccine hesitancy. RESULTS: Among 1735 participants, 41.61% (722/1735) reported COVID-19 vaccine hesitancy. Older age, no other vaccinations in the past 3 years, and having chronic disease history were positively associated with COVID-19 vaccine hesitancy. Structural equation modeling revealed a direct relationship of perceived benefits, perceived risks, and subjective norms with self-efficacy and vaccine hesitancy and an indirect relationship of perceived benefits, perceived risks, and subjective norms with vaccine hesitancy. Moreover, self-efficacy toward COVID-19 vaccination was low. PLWHA had concerns of HIV disclosure during COVID-19 vaccination. Family member support could have an impact on COVID-19 vaccination decision-making. CONCLUSIONS: COVID-19 vaccine hesitancy was high among PLWHA in China. To reduce COVID-19 vaccine hesitancy, programs and strategies should be adopted to eliminate the concerns for COVID-19 vaccination, disseminate accurate information on the safety and efficacy of the COVID-19 vaccine, encourage family member support for COVID-19 vaccination, and improve PLWHA's trust of medical professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".