Side effects of Covishield vaccine among frontline healthcare workers of a tertiary health care center
Bibliographic record
Abstract
Abstract Objectives COVID disease started in the late 2019 and within a short time became a pandemic disease. With the increasing morbidity and mortality all over the world and the therapeutics not doing wonders, scientists were in the attempt to develop vaccines as a mitigating measure. With continuous efforts and developments, different vaccines were developed and rolled out gradually in different countries. Concerns were notable for occurrence of side effects. Hence this study was done to assess the side effects following Covishield vaccination in Nepal at the initial stage. Methods This was a cross-sectional study done via snowball sampling method among healthcare workers at a tertiary medical college hospital in Pokhara, Nepal after obtaining ethical consent from the institutional review committee of the concerned hospital. The proforma was sent via online means through different social media platforms and also printed forms were also given to the respondents. A total of 139 respondents were obtained after removing duplications. The data were entered into SPSS and analyzed using descriptive and inferential statistics. P-value ≤ 0.05 was considered statistically significant. Results Majority (64.7%) were female healthcare workers. More than half (52.3%) used pre-medication in an attempt to avoid the side effects of vaccine. Most (90.6%) reported at least one side effect-local or systemic to the first dose and approximately three-quarter (74.3%) reported side effect to the second dose. Common side effects were pain at injection site, muscle pain, headache, fatigue and weakness. Most of the side effects were higher with the first dose as compared to the second dose. Conclusion Side effects are common with Covishield vaccination, significantly more with the first dose as compared to the second dose. Female gender, younger age and past covid infection were associated with slightly more occurrence of side effects; however were not found to be statistically significant.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".