Acceptance of COVID-19 Vaccine in Pakistan: A Nationwide Cross-Sectional Study
Bibliographic record
Abstract
Introduction The coronavirus disease 2019 (COVID-19) vaccine is available across various countries worldwide, with public-private partnerships ensuring all individuals are vaccinated through a phased approach. Irrespective of the geographical spread, several myths pertaining to the COVID-19 vaccine have stemmed, ultimately limiting the national administration of vaccines and rollouts. This study assessed the acceptance of the COVID-19 vaccine among the general public in Pakistan. Methods A pre-validated questionnaire was administered from January 2021 to February 2021 to assess the public attitude and acceptance of the COVID-19 vaccine. Logistic regression analyses were run to identify factors associated with the acceptance among the population. Results A total of 936 responses were elicited, where 15% perceived their risk of being infected at 20-30% with an overall 70% agreeing to be vaccinated if recommended. Multivariate analysis identified higher acceptance in the male gender, healthcare workers, and students. Of all, 66% respondents chose healthcare workers and public officials, whereas 15.6% chose scientific literature, and 12.9% chose social media as the most reliable source of COVID-19 information. Conclusion Given the relatively greater trust in healthcare providers for information regarding COVID-19, healthcare workers ought to be on the frontline for vaccine campaigns and public outreach efforts, with governmental efforts in addition to the promotion of scientific materials for population-level understanding.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".