Investigating the tendency to use COVID-19 vaccine booster dose in Iran
Bibliographic record
Abstract
INTRODUCTION: Vaccine hesitancy is recognized as a significant public health threats, characterized by delays, refusals, or reluctance to accept vaccinations despite their availability. This study, aimed to investigate the willingness of Iranians to receive booster shots, refusal rate, and their preferred type of COVID-19 vaccine. MATERIALS AND METHODS: This cross-sectional study was conducted over a month from August 23 to September 22, 2022 using an online questionnaire distributed through WhatsApp and Telegram online communities. The questionnaire assessed participants' intent to accept COVID-19 booster vaccination and had no exclusion criteria. Data analysis involved using SPSS version 16.0, with t-tests and chi-square tests used to assess the bivariate association of continuous and categorical variables. A multivariate logistic regression model was built to examine the association between Health Belief Model (HBM) tenets and COVID-19 vaccination intent. The Hosmer Lemeshow Goodness of Fit statistic was used to assess the model's fit, with a p-value > 0.05 indicating a good fit. RESULTS: The survey was disseminated to 1041 adults and the findings revealed that 82.5% of participants expressed a desire to receive the booster dose. Participants who intended to be vaccinated were generally older (46.4 ± 10.9), mostly female (53.3%), single (78.9%), had received a flu vaccine (45.8%). The findings indicated that the HBM items, including perception of COVID-19 disease, perceived benefits of COVID-19 vaccines, COVID-19 safety/cost concerns, preference of COVID-19 vaccine alternatives, and prosocial norms for COVID-19 vaccination, received higher scores among individuals intending to be vaccinated compared to vaccine-hesitant individuals, with statistical significance (p < 0.05). However, the "COVID-19 risk-reduction habits" item had a higher score but did not reach statistical significance (p = 0.167). CONCLUSION: Factors such as lack of trust in the effectiveness of the vaccine, trust in specific vaccine manufacturers, and concerns about side effects of COVID-19 vaccine are among the most important factors. These findings have implications for national vaccination policies, emphasizing the need for policymakers in the health sector to address these factors as vital considerations to ensure the continuity of vaccination as one of the most important strategies for controlling the pandemic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".