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Enregistrement W4391994776 · doi:10.2196/48134

Newspaper Coverage of Hospitals During a Prolonged Health Crisis: Longitudinal Mixed Methods Study

2024· article· en· W4391994776 sur OpenAlexvenueno aff
Frank van de Baan, Rachel Gifford, Dirk Ruwaard, Bram Fleuren, Daan Westra

Notice bibliographique

RevueJMIR Public Health and Surveillance · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiquePublic Relations and Crisis Communication
Établissements canadiensnon disponible
Organismes subventionnairesZonMw
Mots-clésNewspaperThematic analysisPreparednessContent analysisPublic healthPandemicMedicineHealth carePublic relationsPolitical scienceCoronavirus disease 2019 (COVID-19)Qualitative researchBusinessSociologyNursingAdvertisingSocial sciencePathology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: It is important for health organizations to communicate with the public through newspapers during health crises. Although hospitals were a main source of information for the public during the COVID-19 pandemic, little is known about how this information was presented to the public through (web-based) newspaper articles. OBJECTIVE: This study aims to examine newspaper reporting on the situation in hospitals during the first year of the COVID-19 pandemic in the Netherlands and to assess the degree to which the reporting in newspapers aligned with what occurred in practice. METHODS: We used a mixed methods longitudinal design to compare internal data from all hospitals (n=5) located in one of the most heavily affected regions of the Netherlands with the information reported by a newspaper covering the same region. The internal data comprised 763 pages of crisis meeting documents and 635 minutes of video communications. A total of 14,401 newspaper articles were retrieved from the LexisNexis Academic (RELX Group) database, of which 194 (1.3%) articles were included for data analysis. For qualitative analysis, we used content and thematic analyses. For quantitative analysis, we used chi-square tests. RESULTS: The content of the internal data was categorized into 12 themes: COVID-19 capacity; regular care capacity; regional, national, and international collaboration; human resources; well-being; public support; material resources; innovation; policies and protocols; finance; preparedness; and ethics. Compared with the internal documents, the newspaper articles focused significantly more on the themes COVID-19 capacity (P<.001), regular care capacity (P<.001), and public support (P<.001) during the first year of the pandemic, whereas they focused significantly less on the themes material resources (P=.004) and policies and protocols (P<.001). Differences in attention toward themes were mainly observed between the first and second waves of the pandemic and at the end of the third wave. For some themes, the attention in the newspaper articles preceded the attention given to these themes in the internal documents. Reporting was done through various forms, including diary articles written from the perspective of the hospital staff. No indication of the presence of misinformation was found in the newspaper articles. CONCLUSIONS: Throughout the first year of the pandemic, newspaper articles provided coverage on the situation of hospitals and experiences of staff. The focus on themes within newspaper articles compared with internal hospital data differed significantly for 5 (42%) of the 12 identified themes. The discrepancies between newspapers and hospitals in their focus on themes could be attributed to their gatekeeping roles. Both parties should be aware of their gatekeeping role and how this may affect information distribution. During health crises, newspapers can be a credible source of information for the public. The information can also be valuable for hospitals themselves, as it allows them to anticipate internal and external developments.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,371
Score d'incertitude au seuil0,649

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,044
Tête enseignante GPT0,422
Écart entre enseignants0,378 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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