Digital Media Coverage of Respiratory Syncytial Virus-Related News in India: Mixed Methods Content Analysis of Disease Burden and Intervention
Notice bibliographique
Résumé
Background: Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in children younger than 5 years of age. Given the high morbidity and mortality associated with RSV in India, the introduction of a vaccine against RSV will potentially reduce the disease's burden. However, vaccine acceptance is influenced by public perception, which is shaped by information disseminated through media sources. This study aims to explore the landscape of RSV-related news coverage in India's digital media. Objective: This study aims to conduct a comprehensive content analysis to explore the landscape of RSV-related news coverage in India's digital media. Methods: Media content analysis was retrospectively conducted by a digital search for all related news pieces in the trustworthy brands of 4 trusted newspapers (Hindustan Times, The Hindu, The Indian Express, and The Times of India) and 3 news channel websites (India Today, NDTV news, and News 18), between November 1, 2022, and October 31, 2023. A total of 58 news pieces were retrieved using selected keywords, with inclusion criteria encompassing English-language news pieces with RSV-specific content. Two reviewers compiled, coded, and analyzed the content. Quantitative data were analyzed descriptively, while qualitative content analysis assessed the emotional tone and sentiment of the pieces. Results: The findings revealed significant digital media coverage on RSV infection and the potential vaccines. The majority of news pieces (53/58, 91%) discussed RSV signs and symptoms, with 64% (37/58) addressing the disease severity and 36% (21/58) highlighting its seasonal surge. However, only 5% (3/58) focused on diagnostic aids. Additionally, 41% (24/58) of news pieces discussed RSV in the context of COVID-19. Regarding the vaccine, 29% (17/58) of news pieces mentioned it, with 26% (15/58) highlighting manufacturers such as Pfizer and GlaxoSmithKline (GSK). Positive sentiment was found in 35% (20/58) of news pieces, while 43% (25/58) exhibited negative sentiment, often related to the disease burden and severity. Emotional tone analysis revealed that 74% (43/58) of news pieces contained emotional elements, with 58% (25/43) expressing negative emotions (eg, concern and anxiety), particularly about hospitalizations and deaths. In contrast, a positive tone was emulated in the frequent mentions of the RSV vaccines as safe, effective, and approved. Conclusions: The analysis revealed significant coverage of RSV-related news in India's digital media, with a focus on disease severity and hospitalizations. While positive sentiment was expressed in coverage of the RSV vaccine, negative sentiments dominated discussions on the disease burden. However, considering the limited number of news pieces, the study highlights the need for improved media coverage to raise awareness about the disease and its preventive strategies. Further research should explore the implications of the overlap between RSV and COVID-19 in media coverage and the limited focus on RSV diagnostics, with a focus on understanding how these factors impact public health outcomes.
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 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 | 0,004 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».