Comparing Academic and Community Critical Care Clinicians’ Knowledge and Attitudes, ARDS Management Practices, and the Effect of the COVID-19 Pandemic: A Multicenter, Multidisciplinary Survey
Notice bibliographique
Résumé
Abstract RATIONALE: Acute respiratory distress syndrome (ARDS) has high morbidity and mortality, yet barriers to recognizing ARDS and adopting evidence-based treatment strategies exist. Prior studies have not investigated whether these barriers differ between academic and community critical care settings. METHODS: We conducted a survey in 2020-2021 of critical care physicians, nurses, advanced practice providers (APPs), and respiratory therapists (RTs) in six academic and nine community hospitals in the United States and Canada. The survey included multiple domains: knowledge of ARDS, reported ARDS management, changes associated with the COVID-19 pandemic, general attitudes toward adoption of evidence-based practice, perceived system and knowledge barriers to ARDS management, team- and ICU-based culture, and interprofessional communication. Statistical significance was adjusted for multiple comparisons. RESULTS: 1,906 clinicians responded to the survey (53% response rate). There were important differences between academic and community clinicians in several domains (Table 1). Community physicians and nurses had significantly higher culture scores compared to academic physicians and nurses (P<0.005 for both comparisons). Community nurses had a higher communication score compared to academic nurses, whereas academic nurses and RTs had higher knowledge scores compared to community nurses and RTs, respectively. Academic physicians, nurses, and RTs reported caring for ARDS patients more frequently than their community counterparts, with academic physicians being almost twice as likely to care for ARDS patients every day/several days per week compared to community physicians (academic: 64.4%, community: 34.6%; P<0.001). Community physicians, nurses, and RTs all reported a higher number of changes in practice due to the COVID-19 pandemic compared to academic clinicians (P<0.005). For example, of the 22 potential changes during the COVID-19 pandemic, community physicians reported a higher mean (SD) number of moderate or large increases, or changes to or adoption of new practices, compared to academic physicians (community: 13.7 [2.7] vs. academic: 11.8 [4.3], P=0.0031). As whole clinician groups (academic and community together), nurses and RTs perceived both culture and communication to be lower quality compared to physicians (P<0.0083). CONCLUSIONS: In a large, multidisciplinary survey of critical care clinicians, differences were reported between academic and community clinicians’ culture, communication, and knowledge. The COVID-19 pandemic had a greater impact on community ICU organization and structure, and ARDS management. Multifaceted implementation strategies should target knowledge, culture, and communication differently in academic and community settings, and for different clinician groups.
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 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».