Perceived Risk and Associated Factors towards COVID-19 infection among the residents of Ondo State, Southwest Nigeria
Notice bibliographique
Résumé
BackgroundPerceived risk is an important factor in understanding why and how a population adopts health-prevention interventions. When diseases are perceived as low-risk, motivation to use and adhere to prevention interventions is low, which can aid the spread of highly infectious diseases such as COVID-19. In this study, we assessed risk perception and its associated factors towards COVID-19 infection among the residents of Ondo State, southwest Nigeria. MethodsA community-based, cross-sectional study design using a multi-stage sampling technique was used to recruit 593 heads of households in three local government areas (LGA) in Ondo State. Data were collected using an interviewer-administered questionnaire which captured information on sociodemographic variables, knowledge of COVID-19 prevention, COVID-19 infection prevention and control practices, and risk perception from September 1 to 30, 2021. ResultsThe respondents were mostly males 357 (60.2%). The mean age of respondents was 37.5±14.7 years with 409 (69%) between the age group of 20 to 40 years. Slightly more than one quarter of respondents were civil servants and 78.4% were of Yoruba ethnicity. More than three quarters, 522 (88%), of the respondents had good knowledge of COVID-19 and its preventive measures. The mean risk perception score was 49.0±12.1. Respondents who were civil servants, had good knowledge of COVID-19 and its preventive measures, had lower household income, and were of Yoruba ethnicity had significantly higher risk perception towards COVID-19 infection compared to other groups. Higher risk perception was associated with preventive measures, such as handwashing. ConclusionOur study shows a high-risk perception towards COVID-19 infection among residents in Ondo state, Nigeria. However, there were significant differences between varying knowledge levels, ethnic groups and civil versus non-civil servants. In view of this, we recommend intensified risk communication interventions targeting these groups to improve their risk perception to change health-protective behaviour towards COVID-19 infection.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».