Social Attitude Towards COVID-19 Vaccine 1 Year After The Pandemic
Bibliographic record
Abstract
Objective: To increase vaccine awareness, we aimed to determine individuals’ knowledge and behavioral approach to the COVID-19 vaccine. Methods: The data of this cross-sectional study were obtained online between June and July 31, 2021. One thousand one hundred seventy-six people over the age of 18 were included in the study. The researchers developed a data collection form consisting of 27 questions. Mean±standard deviation and median (1st quarter-3rd quarter) values, numbers, and percentages were used to summarize the data. Chi-square (χ2) test was used to show the relationship between categorical variables. Independent predictors of participants’ vaccine hesitancy/rejection were analyzed using logistic regression. Statistically, data with p<0.05 were considered significant. Results: A total of 1176 people, 55.7% of whom were women, with a mean age of 39.75±11.27 years, were included in the study. 71.6% of the participants were married, and 78.9% had a university/postgraduate degree. 9.7% of the participants stated that they were hesitant about the COVID-19 vaccine, and 7.1% refused the COVID-19 vaccine or would not be vaccinated when it was their turn. According to the logistic regression model established to examine the factors that may affect vaccine rejection; Age, the resources used to obtain information about the vaccine, the thought that it would not protect for two years, or the vaccine side effects were high, and the most effective way to get rid of the pandemic was not vaccination, were determined as the factors affecting vaccine rejection. Conclusion: As a result of the research, it was found that the participants had a positive attitude towards the COVID-19 vaccine. It was determined that 9.7% of the study group had vaccine hesitancy, and 7.1% had vaccine rejection. Keywords: Vaccine hesitancy, vaccine rejection, COVID-19 vaccines
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".