Self-reported side effects of COVID-19 vaccines among the public
Bibliographic record
Abstract
Background The safety, side effects and efficacy profile of COVID-19 vaccines remain subjects of ongoing concern among the public in Malaysia. The aim of this study was to determine the types of adverse effects following immunisation with COVID-19 vaccines and the differences based on various types of COVID-19 vaccines to raise public awareness and reduce vaccine hesitancy among the public.Methods A total of 901 Malaysian adults (≥18 years) who received various COVID-19 vaccines were selected to participate in our cross-sectional study through an online survey between December 2021 and January 2022.Results A total of 814 (90.3%) of the participants reported ≥1 side effect following COVID-19 immunisation. Of these, the predominant symptoms were swelling at the injection site (n = 752, 83.5%), headache (n = 638, 70.8%), pain or soreness at the injection site (n = 628, 69.7%), fatigue or tiredness (n = 544, 60.4%), muscle weakness (n = 529, 58.7%) and diarrhea (n = 451, 50.1%). Recipients of the Pfizer-BioNTech (Comirnaty ®) vaccine reported the highest number of adverse effects (n = 355, 43.6%), followed by mixed COVID-19 vaccines (n = 254, 31.2%), the Oxford-AstraZeneca (ChAdOx1-®[recombinant]) vaccine (n = 113, 13.9%) and the Sinovac (CoronaVac®) vaccine (n = 90, 11.1%). The study showed that individuals who reported significantly more side effects were of elderly age, female gender and high educational level [P value < 0.05]. Mixed COVID-19 vaccine recipients also reported significantly more local and systemic symptoms after the first dose and third dose when compared with other single vaccine recipients.Conclusion This study demonstrated the types of self-reported adverse effects following immunisation with single and mixed COVID-19 vaccines. These findings may provide the side effects of different COVID-19 vaccines with the hope of educating the public on the safety profiles of these vaccines and reducing vaccine hesitancy among the public.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".