Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine
Bibliographic record
Abstract
The purpose of this research was to determine the factors influencing the refusal of a coronavirus disease (COVID- 19) vaccine among adult students from Arab Open University in Kuwait (AOU) and their families and to study the trends of reluctant participants. A questionnaire was conducted (n = 691; aged 12 and older). Significant factors and the tendency of hesitant participants to accept or reject the vaccine were explored by applying a cleaning and coding process, a rough set theory (RS), a decision tree (DT) classifier, and a p-value. Overall, 18.4% of the participants reported refusing to receive a COVID-19 vaccine, while 17.2% were uncertain. The study shows that hesitant subjects represent a tendency to accept vaccination. Of the vaccine-refusal participants, subjects aged 18-29, suffer from chronic disease, were infected with COVID-19, were vaccinated against seasonal flu, and had concerns about receiving a COVID-19, representing 44.1%, 21.05%, 16.76%, 54.33%, and 70.08%, respectively. Overall, 18.4% of the participants demonstrated a refusal to receive a COVID-19 vaccine and 17.2% are hesitant. Factors influencing the level of acceptance/rejection of the vaccine were determined. The results showed that hesitant participants have a strong tendency to accept the vaccine (81.82%). Since vaccination is an important strategy to reduce the spread of the COVID-19 pandemic, the ministry of public health must immediately address the significant factors for the acceptance/rejection of the vaccine, as well as the trend of hesitant participants toward the acceptance of the vaccine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".