Knowledge and Perception of Adult Nigerian Men Towards Covid-19 Vaccination: A case study in Southwest Nigeria.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».