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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".