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A Multidisciplinary Survey Comparing Academic and Community Critical Care Clinicians’ Acute Respiratory Distress Syndrome Practice and the COVID-19 Pandemic

2025· article· en· W4411556720 on OpenAlexaffabout
Diane Masket, Carey C. Thomson, André Carlos Kajdacsy-Balla Amaral, Catherine L. Hough, Nicholas J. Johnson, David Kaufman, Jonathan Siner, Jennifer P. Stevens, Lipisha Agarwal, Peymaan Banankhah, M Casasola, Adriana Flores, Brenda Garcia, Joseph Khoory, Giulia Paliotti, Arashdeep Rupal, Harpreet Singh, Alex Walker, Joe Watson, Curtis H. Weiss

Bibliographic record

VenueAnnals of the American Thoracic Society · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineCoronavirus disease 2019 (COVID-19)PandemicARDS2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Multidisciplinary approachBetacoronavirusCoronavirus InfectionsMEDLINEIntensive care medicineVirologyInfectious disease (medical specialty)PathologyInternal medicineDiseaseOutbreakLung

Abstract

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Abstract Rationale Barriers to recognizing and treating acute respiratory distress syndrome (ARDS) exist. Prior studies have not investigated whether these barriers differ between academic and community settings or whether there were differences in critical care clinicians’ reported ARDS management strategies during the coronavirus disease (COVID-19) pandemic. Objectives Grounded in the Consolidated Framework for Implementation Research, we sought to determine whether there are differences between academic and community critical care clinicians in their team- and intensive care unit (ICU)-based culture; interprofessional communication; knowledge, attitudes, and perceived barriers to ARDS recognition and management; and ICU organization and ARDS management associated with the COVID-19 pandemic. Methods Multidisciplinary survey from September 2020 to April 2021 of critical care physicians, nurses, advanced practice providers, and respiratory therapists (RTs) in six academic and nine community hospitals across the United States and Canada. Individual item and cumulative domain scores were compared between academic and community clinicians. Statistical adjustment was performed for multiple comparisons. Results A total of 1,906 clinicians responded to at least one survey item (53% response rate). Mean (standard deviation [SD]) culture scores were higher for community physicians versus academic physicians (5.3 [1.8] vs. 4.4 [2.0]; P < 0.001) and community nurses versus academic nurses (4.4 [2.2] vs. 3.8 [2.1]; P = 0.007). Academic nurses and RTs had higher knowledge scores than community nurses and RTs (P < 0.001 for each comparison). Community physicians, nurses, and RTs reported higher mean (SD) number of changes in ICU organization and practice during the COVID-19 pandemic than academic clinicians (e.g., community physicians: 13.7 [2.7] changes vs. academic physicians: 11.8 [4.3] changes; P = 0.001). Although academic physicians, nurses, and RTs were approximately twice as likely to care for patients with ARDS daily or several days per week compared with community clinicians, ARDS management, attitudes, and belief in evidence was similar between academic and community clinicians in most respects. Conclusions A large, multidisciplinary survey identified differences between academic and community critical care clinicians’ culture and knowledge in the care of patients with ARDS. The COVID-19 pandemic had a greater impact on community ICU organization and ARDS management. Multifaceted implementation strategies should target implementation barriers differently in academic and community settings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.286
GPT teacher head0.529
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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