COVID-19 vaccination acceptance in Iran, a nationwide survey on factors associated with the willingness toward getting vaccinated
Bibliographic record
Abstract
Background: In the name of extensive vaccine uptake, understanding the public's attitude, perception, and intent toward COVID-19 vaccination is a significant challenge for public health officials. Methods: A cross-sectional survey via an online questionnaire rooted in the Health Belief Model and Integrated Behavioral Model was conducted to evaluate COVID-19 vaccination intent and its associated factors. Factor analysis and multivariate logistic regression were operated to be satisfactory. Results: Among the 4,933 respondents, 24.7% were health care workers, and 64.2% intended to accept COVID-19 vaccination. The adjusted odds (aOR) of COVID-19 vaccination intent was higher for individuals with greater exposure to social norms supportive of COVID-19 vaccination (aOR = 3.07, 95% Confidence Interval (CI) = 2.71, 3.47) and higher perceived benefits of COVID-19 vaccination (aOR = 2.9, 95% CI = 2.49, 3.38). The adjusted odds of vaccination intent were lower for individuals with greater COVID-19 vaccine safety concerns (aOR = 0.28, 95%CI = 0.25, 0.31). Lower vaccination intent was also associated with increasing age ((aOR = 0.99, 95% CI = 0.98, 0.999), female sex (aOR = 0.76, 95% CI = 0.65, 0.88), and working in the health care field (aOR = 0.75, 95% CI = 0.63, 0.9). Conclusions: The odds of COVID-19 vaccination intent were higher three or more times among those with a greater belief in vaccine effectiveness, lower concerns about vaccine safety, and greater exposure to cues to vaccinate, including from doctors. This last finding is concerning as vaccine acceptance was surprisingly lower among health care workers compared to others. The remarkable results of factor analysis and reliability of the questionnaire may encourage local health authorities to apply it to their regional population.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".