Hesitancy of Covid-19 Vaccination among Health Workers (other than Doctors) in a Tertiary Hospital in South-South, Nigeria
Bibliographic record
Abstract
Background: Coronavirus Disease 2019 (COVID-19) is a disease of the respiratory system that is caused by the Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2). It was declared a pandemic by the World Health Organisation on the 11th of March, 2020. Objective: To assess the reasons behind the low turnout of health workers (other than doctors) for COVID-19 vaccination in the Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria. Materials and Methods: This study was carried out at the Federal Medical Centre, Yenagoa between 1st and 23rd April, 2021. It was a descriptive cross-sectional study. The study population consisted of 182 health workers (excluding doctors) from all departments/units in the hospital. The data were collected with a predesigned questionnaire, and were analysed using IBM SPSS 23.0 version. Results: About three-quarter were females (74.7%), and close to half were aged ≤35 years (47.8%). The respondents were Nurses, Pharmacists, Medical laboratory scientists, and Non-clinical officers. Only 27.4% took the vaccine. Most of those who refused the vaccine did so because they wanted to see what would happen to those who received the vaccine (70.5%). Others felt the vaccine has not gone through enough clinical trials (62.1%). Conclusion: In this study, there was very poor turnout of health workers for COVID-19 vaccination. The factors that influenced acceptance of the vaccine were mainly COVID-19 related features. These findings suggest that people who have had personal experiences with the disease have a better understanding of the gravity of the situation, and hence are more likely to accept vaccination against the disease.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".