Coronavirus Disease 2019 (COVID-19): Knowledge, attitudes, practices (KAP) and misconceptions in the general population of Katsina State, Nigeria
Notice bibliographique
Résumé
Abstract Introduction Over six million cases of Coronavirus Disease 2019 (COVID-19) were reported globally by the second quarter of 2020. The various forms of interventions and measures adopted to control the disease affected people’s social and behavioural practices. Aim This study aims to investigate COVID-19 related knowledge, attitudes and practices (KAP) as well as misconceptions in Katsina state, one of the largest epicentres of the COVID-19 outbreak in Nigeria. Methods The study is a cross-sectional survey of 722 respondents using an electronic questionnaire through the WhatsApp media platform. Results One thousand five hundred (1500) questionnaires were sent to the general public with a response rate of 48% (i.e. 722 questionnaires completed and returned). Among the respondents, 60% were men, 45% were 25-39 years of age, 56% held bachelor’s degree/equivalent and above and 54% were employed. The study respondents’ correct rate in the knowledge questionnaire was 80% suggesting high knowledge of the disease. A significant correlation ( P < 0.05) exists between the average knowledge score of the respondents and their level of education (τ b = 0.16). Overall, most of the respondents agreed that the COVID-19 will be successfully controlled (84%) and the Nigerian government would win the fight against the pandemic (71%). Men were more likely than female ( P < 0.05) to have recently attended a crowded place. Being more educated (bachelor’s degree or equivalent and above vs diploma or equivalent and below) is associated with good COVID-19 related practices. Among the respondents, 83% held at least one misconception related to COVID-19, with the most frequent being that the virus was created in a laboratory (36%). Respondents with a lower level of education received and trust COVID-19 related information from local radio and television stations and respondents at all levels of education selected that they would trust health unit and health care workers for relevant COVID-19 information. Conclusion Although there is high COVID-19 related knowledge among the sample, misconceptions are widespread among the respondents. These misconceptions have consequences on the short- and long-term control efforts against the disease and hence should be incorporated in targeted campaigns. Health care related personnel should be at the forefront of the campaign.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».