Knowledge and Perception of Adult Nigerian Men Towards Covid-19 Vaccination: A case study in Southwest Nigeria.
Bibliographic record
Abstract
Abstract Introduction Corona virus illness 2019 (COVID-19), a pandemic event, began in the first quarter of 2020 and affected nearly every country on earth. Several researches on Covid-19 knowledge, attitude, and acceptance of religion in several Nigerian locations have been published. There is a scarcity of data on adult Nigerian men's perceptions and awareness of COVID-19 in relation to immunization. As a result, this study looks into the vaccination knowledge and attitudes of adult Nigerian men in south-west Nigeria. Methods The study is an analytic cross-sectional survey with structured online questionnaires Google form that was administered to the people of Oyo, Osun, and Lagos in Nigeria's south-western states. The study enlisted 309 participants, who were chosen at random after providing informed permission. The link between knowledge of COVID-19 and social demographics factors, knowledge of the vaccination, perception of the vaccine, and awareness and misinformation about COVID-19 was scored using percentages and subjects. The variable percentage scores were classified as good or poor. Chi-Square was utilized to determine independent determinants of COVID-19 knowledge and perception, and SPSS was used for statistical analysis. Results According to the socio-demographic characteristics study, the mean age was 28.36.8 years, 52.4 percent of respondents were male, 28.5% were from Osun state, and (96.8%) had university education. 91.9% of responders had an excellent understanding of the COVID-19 vaccination. The general perception of the respondents was positive, with 240 (77.7%) of the respondents favoring the COVID-19 vaccination. The majority of respondents (88.3%) believe that developing a vaccine can aid in the fight against the pandemic, while 65.5% believe that the COVID-19 vaccine is safe based on what they know. Less than half of the respondents (45.6%) answered that if they knew the COVID-19 vaccination could influence their daily life activities after taking it, they would avoid taking it. They will still make themselves available for it, and the majority (64.1%) believe that COVID-19 will be successfully controlled with the vaccination. To the best of their knowledge, less than half of the respondents (44.0%) believe the Corona virus is a biological weapon designed by the Chinese government, while 15.2% believe it is a virus designed by the pharmaceutical industry to sell their drugs, and a similar (15.2%) believe the virus is an exaggeration by the news media to cause fear and panic. When asked how the virus is most commonly transmitted, 95.8% said they were exposed to airborne droplets through breathing, sneezing, or coughing, while 28.2% said they were exposed through kissing, hugging, sex, or other sexual contact. Another group of respondents (8.1%) indicated that HPV is spread by drinking contaminated water or eating infected food, while more than half (59.5%) stated that it can be spread by touching contaminated objects or surfaces. When asked how the virus can be prevented, fifty-six (18.1%) of respondents said that Africa's hot heat can prevent the transmission, while the majority (91.9%) said that frequent hand washing and social separation can. A few (6.8%) believe that consuming Chloroquine tablets and medicines can help avoid the illness. While fumigation and spraying bus stations and other public locations were mentioned as ways to prevent the virus by around one-third (31.4%) of those polled. Furthermore, twenty-seven (8.7%) listed ingesting gins, garlic, ginger, herbal mixture, traditional meals and soup as a means to prevent the infection. In terms of COVID-19 disease perception, the majority of respondents (90.6%) believed that it is possible to die from the Coronavirus, while a comparable proportion (89.6%) agreed that the complete meaning of COVID-19 is Corona Virus Disease 19. Conclusion This study found that national public health information advocacy and campaigns in Southwest Nigeria were extremely effective in increasing understanding of COVID-19 and decreasing beliefs about disinformation, immunization, and mass medication administration programs. However, more can be done using social media and telecommunication to combat disinformation about immunization in more Nigerian and African regions and rural settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".