COVID-19 Vaccine Acceptance and Its Risk Factors in Iranian Health Workers 2021.
Bibliographic record
Abstract
Background: Ensuring vaccine acceptance in societies is a growing challenge for healthcare systems worldwide. This study aimed to identify factors associated with vaccine acceptance rates. Methods: , 2021, just before the release of the COVID-19 vaccine in Shiraz, Iran. Independent variables included age, gender, occupation, history of COVID-19 infection, underlying diseases, and source of information. The willingness to be vaccinated was the dependent variable. A logistic regression analysis was performed to determine the relationship between different variables and the willingness to receive the COVID-19 vaccine. The significance level was set at less than 0.05. The data were analyzed using SPSS software version 21. Results: Of 2,699 healthcare respondents, 70.3% indicated a willingness to receive the COVID-19 vaccine, of whom 49.2% preferred to receive a foreign vaccine and 24.68% desired to receive an Iranian vaccine. The women were more willing to receive the vaccine (67.6%) than the men (78.2%). Based on the results of logistic regression, gender (P<0.001) and job (P=0.005) were the most important associating factors to the willingness to receive the COVID-19 vaccine. Conclusion: Although the majority of participants were willing to receive the COVID-19 vaccine, 29.6% were not yet ready. Women's healthcare providers were more hesitant to recommend the vaccine. As a result, the findings of this study can help policymakers and decision-makers in the field of health, treatment, and prevention of COVID-19 in raising the level of vaccination awareness among healthcare workers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".