Changes in Perceptions and Use of Mobile Technology and Health Communication in South Africa During the COVID-19 Lockdown: Cross-sectional Survey Study
Notice bibliographique
Résumé
BACKGROUND: In late March 2020, South Africa implemented a 5-stage COVID-19 Risk Adjusted Strategy, which included a lockdown that required all residents to remain home to prevent the spread of COVID-19. Due to this lockdown, individuals have been forced to find and use alternatives for accomplishing tasks including shopping, socializing, working, and finding information, and many have turned to the internet and their mobile devices. OBJECTIVE: This study aimed to describe how South Africans consume and internalize information surrounding the COVID-19 outbreak in order to determine whether the COVID-19 lockdown and social isolation have influenced technology behavior, particularly in terms of health communication and information. METHODS: From June 24 to August 24, 2020, people in South Africa were invited to complete a survey through the Upinion mobile app, an online data collection resource. The survey collected information on demographics, and technology use during the lockdown, and COVID-19 knowledge. RESULTS: There were 405 participants, of which 296 (73.06%) were female. A total of 320 (79.01%) participants had a tertiary school education, 242 (59.75%) were single, and 173 (42.72%) had full-time employment. The lockdown forced 363 (89.63%) participants to use more technology, especially for work (n=140, 24.05%) and social media/communication (n=133, 22.85%). Security or privacy issues (n=46, 38.98%) and unfamiliarity with technology (n=32, 27.12%) were identified as the most common issues faced by the 127 (31.36%) participants who were unsure about using technology prior to the lockdown. Almost all participants (n=392, 96.79%) stated that they would continue using technology after the lockdown. Multimedia (n=215, 53.09%), mobile phone content (n=99, 24.44%), and health organizations and professionals (n=91, 22.47%) were the main sources of COVID-19 information. Most participants (n=282, 69.63%) felt that they had enough information. Two-thirds (n=275, 67.90%) of participants stated that they had used their mobile phones for health information before the lockdown, with web searches (n=109, 26.91%), social media (n=58, 14.32%), and government and institutional websites (n=52; 12.84%) serving as their main sources of information. Overall, the mean COVID-19 knowledge score was 8.8 (out of 10), and 335 (82.72%) had adequate knowledge (scored ≥8). Males were less likely to identify the correct transmission routes, and single participants were less likely to identify the signs and symptoms of the coronavirus. Tertiary school graduates were 4 times more likely to correctly identify the routes and 2 times more likely to identify how to stop the spread of the virus. People aged 43-56 years were 4 times more likely to identify how the coronavirus can be prevented, and participants ≥57 years were 2.6 times more likely to obtain a knowledge score of 10 when compared to those under 29 years of age. CONCLUSIONS: This study has shown that the COVID-19 lockdown has forced people to increase technology use, and people plan to continue using technology after the lockdown is lifted. Increased technology use was seen across a variety of fields; however, barriers including privacy, unfamiliarity, and data costs were identified. This population showed high COVID-19 knowledge, although the use of web searches and social media, instead of government and institutional websites, increases the potential for health misinformation to be spread.
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».