Health-Care Personnel's Perspective on COVID-19 Vaccination – A Cross-Sectional Study
Bibliographic record
Abstract
Introduction: The COVID pandemic was a modern world disaster which had physical, psychological, and economical impact among the people. This made the governing agencies and others to rollout vaccine in a prompt basis. The objectives were to assess the attitude of health-care personnel toward COVID-19 vaccination using online survey and to assess the willingness of COVID-19 vaccination and factors affecting it among health-care personnel. Materials and Methods: We conducted a cross-sectional study using web-based platforms among 471 health-care personnel's within a period of a month (December 2020–January 2021). The study was conducted after obtaining institution ethic committee approval and informed consent. The questionnaire contains sociodemographic detail, COVID profile section, and questions which reveal the beliefs and attitude toward vaccination particularly COVID-19. The data collected was entered in Microsoft Excel and analyzed using SPSS version 16 software. Results: Among the participants, 56 (11.9%) were diagnosed with COVID-19 and 119 (25.3%) were not willing to take vaccine. Participants who were hesitant about the role of vaccine in immunity, afraid of side effects, doubtful about effectiveness and protection and who doubt about the production involving cost and supply have showed unwillingness to vaccination (P < 0.05). Conclusion: A quarter of the present study population showed unwillingness to take COVID vaccine, and evidence of uncertainty about the vaccine safety and production was exposed in the study. The results should be looked upon gravely as the issues appeared here can be maximized when the vaccine rollout happens in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".