Contributions of Trustworthiness, Health Literacy, and Self-Efficacy in Communicating With COVID-19 Vaccine–Hesitant Audiences: Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: Large-scale health communication challenges during the COVID-19 pandemic, such as widespread misinformation and distrust in health care professionals, have influenced reluctance to take the COVID-19 vaccine, also known as vaccine hesitancy. Trust in health professionals, adequate health literacy, and high self-efficacy are key components of actively pursuing preventative and protective health care measures. These factors may be associated with intentions to seek and complete a COVID-19 vaccine dosing. OBJECTIVE: The objective of this analysis was to identify factors associated with COVID-19 vaccine hesitancy. METHODS: In February 2021, US adults (N=5872) responded to a web-based survey on COVID-19 vaccine hesitancy and components of health communication (trust in sources of health information, health literacy, and self-efficacy). Multivariable logistic regression models were used to explore associations between these factors and vaccine hesitancy while adjusting for key demographics. We hypothesized that low levels of trust, health literacy, and self-efficacy would be associated with increased vaccine hesitancy. RESULTS: The adjusted odds of vaccine hesitancy was greater among those who placed little to no trust in health professionals compared to those who held a lot of trust (adjusted odds ratio [AOR] 8.54, 95% CI 6.52-11.19). The odds of vaccine hesitancy was also greater among those who felt frustrated about finding health information compared to those who did not (AOR 2.10, 95% CI 1.62-2.70). Participants who had little to no confidence in receiving health advice or information had greater odds of vaccine hesitancy compared to those who had a lot of confidence (AOR 3.05, 95% CI 2.34-3.97). CONCLUSIONS: This study underscores the importance of trust between health professionals and their patients, and a need for improving health literacy regarding vaccines. Perceptions of mistrust and low levels of health literacy were associated with high levels of vaccine hesitancy, providing empirical support of framing these factors as perceived barriers to vaccine uptake.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".