COVID-19 Vaccine Hesitancy among Young Adults in Saudi Arabia: A Cross-Sectional Web-Based Study
Bibliographic record
Abstract
Ending the COVID-19 pandemic requires achieving herd immunity, either by previous infection or by vaccination. However, concerns about the COVID-19 vaccine are growing around the globe. The current study was conducted to investigate young the adult population's hesitancy towards the vaccine. The study used a prospective cross-sectional design. Data was collected using an online self-administered questionnaire. A total of 862 Saudi adults participated. Information was gathered on the participants' perspectives towards the severity and susceptibility of the COVID-19 infection, reasons for their hesitancy to receive the vaccine, perceived benefits, and reasons for action. Just under a quarter (19.6%) of respondents had previously tested positive for COVID-19. A small minority of the participants had already received the vaccine (2.1%), while 20.3% had registered in the Sehaty app (application) to receive the vaccine. Just under half of them (48%) will take the vaccine when mass vaccination is achieved and approximately the same number (46.7%) will only take it if it is made mandatory. Vaccine reluctance is highly prevalent among the general public in Saudi Arabia during the COVID-19 pandemic. While many are aware of a high likelihood of getting the infection, the efficacy and safety of the COVID-19 vaccine were reported as barriers to vaccination.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".