Effects of COVID-19 Illness and Vaccination Infodemic Through Mobile Health, Social Media, and Electronic Media on the Attitudes of Caregivers and Health Care Providers in Pakistan: Qualitative Exploratory Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has had a significant impact on different countries because of which various health and safety measures were implemented, with digital media playing a pivotal role. However, digital media also pose significant concerns such as misinformation and lack of direction. OBJECTIVE: We aimed to explore the effects of COVID-19-related infodemics through digital, social, and electronic media on the vaccine-related attitudes of caregivers and health care providers in Pakistan. METHODS: This study employs a qualitative exploratory study design with purposive sampling strategies, and it was conducted at 3 primary health care facilities in the province of Sindh, Pakistan. Seven focus group discussions with health care providers and 60 in-depth interviews with caregivers were conducted using semistructured interviews through virtual platforms (ConnectOnCall and Zoom). Transcripts were analyzed through thematic analysis. RESULTS: Our study reveals the pivotal role of electronic media, mobile health (mHealth), and social media during the COVID-19 pandemic. Four major themes were identified: (1) sources of information on COVID-19 and its vaccination, (2) electronic media value and misleading communication, (3) mHealth leveraging and limitations during COVID-19, and (4) social media influence and barriers during COVID-19. Health care providers and caregivers reported that the common sources of information were electronic media and mHealth, followed by social media. Some participants also used global media for more reliable information related to COVID-19. mHealth solutions such as public awareness messages, videos, call ringtones, and helplines promoted COVID-19 prevention techniques and vaccine registration. However, the overwhelming influx of news and sociobehavioral narratives, including misinformation/disinformation through social media such as WhatsApp, Facebook, and Twitter, were found to be the primary enablers of vaccine-related infodemics. Electronic media and mHealth were utilized more widely to promote information and communication on the COVID-19 pandemic and vaccination. However, social media and electronic media-driven infodemics were identified as the major factors for misinformation related to COVID-19 and vaccine hesitancy. Further, we found a digital divide between the urban and rural populations, with the use of electronic media in rural settings and social media in urban settings. CONCLUSIONS: In a resource-constrained setting like Pakistan, the usage of mHealth, social media, and electronic media for information spread (both factual and mis/disinformation) related to COVID-19 and its vaccination had a significant impact on attitudes toward COVID-19 vaccination. Based on the qualitative findings, we generated a model of digital communications and information dissemination to increase knowledge about COVID-19 and its prevention measures, including vaccination, which can be replicated in similar settings for other disease burdens and related infodemics. Further, to mitigate the infodemics, both digital and nondigital interventions are needed at a larger scale.
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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 | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 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 ».