An Insight Into the Acceptance and Hesitancy of COVID-19 Vaccines in Pakistan: A Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: COVID-19 vaccines are found to be effective interventions to tackle COVID-19. However, the hesitancy towards its acceptance has been rising in Pakistan. This study highlights the opinion of the general population in Pakistan regarding the acceptance and hesitancy of COVID-19 vaccination. METHODS: A descriptive cross-sectional survey study was conducted among Pakistanis from December 2021 to January 2022. Adult respondents that have and have not received COVID-19 vaccinations were included in this study. Data collection was obtained through questionnaires that assessed acceptance and hesitancy toward COVID-19 vaccines. Statistical analysis was performed using IBM SPSS software version 25 for Windows. RESULTS: We obtained 367 respondents with 333 respondents completing the questionnaire. There were 259 respondents who have been vaccinated. A total of 67.9% of responses agreed that vaccines could control the COVID-19 pandemic. The reasons for not getting vaccination were afraid of adverse effects (48.6%) and COVID-19 vaccines not being tested thoroughly (30.9%). The main reason for vaccine acceptance was awareness about vaccines (23.1%), a belief that vaccines can stop severe COVID-19 disease (16.8%), and self-protection (14.7%). CONCLUSION: Most Pakistanis agreed that vaccines could manage the pandemic. Vaccine acceptance was contributed by the awareness and belief regarding the protective effects of vaccines while vaccine hesitancy was due to the public's doubt about the vaccines' side effects and testing. The Pakistan government should focus on emphasizing knowledge about vaccines, educating the vaccines' adverse effects, and utilizing social media in doing so.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".