Perception about COVID-19 vaccine among patients at the federal medical centre, Yenagoa, South-South Nigeria
Bibliographic record
Abstract
Background: The severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) is the causative organism of the coronavirus disease 2019 (COVID-19), which is a respiratory disease that was first identified in December, 2019 in Wuhan, China. Objective was to determine the perception of the COVID-19 among patients at the Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria.Methods: This study was carried out at the Federal Medical Centre, Yenagoa between 4th January and 15th February, 2021. It was a descriptive cross-sectional study. The study population consisted of 1,000 consecutive patients that presented to the various out-patients departments of the hospital. Written informed consent was obtained. The data were collected with a predesigned questionnaire, and were analysed using statistical software (SPSS for windows® version 23, SPSS Inc.; Chicago, USA).Results: Out of 1,000 participants, only a quarter of the participants (24.6%) indicated willingness to take the COVID-19 vaccine when available in Nigeria. About one-tenth of the participants have had loss of sense of taste and smell (11.7%), and think they possibly may have been infected with the COVID-19 (10.8%) in the recent past. Among those that were unwilling to take the COVID-19 vaccine, 14.2%, 9.0% and 7.5% thought that hydroxychloroquine, azithromycin and septrin respectively, are safe alternatives to the vaccine.Conclusions: Although it is known that hypothetical choices may not always reflect real life decision, it is important for policy makers and stakeholders to pay more attention on health education and campaign, targeted at addressing the misconception about COVID-19 vaccine.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".