Understanding ICU Nursing Knowledge, Perceived Barriers, and Facilitators of Sepsis Recognition and Management: A Cross-Sectional Study.

Importance: Nursing workforce changes, knowledge translation gaps, and environmental/organizational barriers may impact sepsis recognition and management within the ICU.

Objectives: To: 1) evaluate current ICU nursing knowledge of sepsis recognition and management, 2) explore individual and environmental or organizational factors impacting nursing recognition and management of sepsis using the Theoretical Domains Framework (TDF), and 3) describe perceived barriers and facilitators to nursing recognition and management of patients with sepsis.

Design, Setting, And Participants: This cross-sectional survey was administered to nurses working in four general system ICUs between October 24, 2023, and January 30, 2024.

The impact of a three-tiered model of nursing redeployment during the COVID-19 pandemic: A cross-sectional study.

Objective: The COVID-19 pandemic resulted in extreme system pressures, requiring redeployment of nurses to intensive care units. We aimed to assess the impacts of a 3-tiered pandemic surge model on nurses working in intensive care units during the COVID-19 pandemic.

Methodology: In this cross-sectional study, 931 nurses (464 intensive care and 467 redeployed nurses) who worked within four adult units in Western Canada during pandemic surge(s) were invited via email to participate in a survey.

Impact of the COVID-19 Pandemic on Nurses Working in Intensive Care Units: A Scoping Review.

Background: The COVID-19 pandemic resulted in significant system strain, requiring rapid redeployment of nurses to intensive care units. Little is known about the impact of the COVID-19 pandemic and surge models on nurses.

Objective: To identify the impact of the COVID-19 pandemic on nurses working in intensive care units.

Climate Change Mitigation, Air Pollution, and Environmental Justice in California.

Climate change mitigation policies can have significant co-benefits for air quality, including benefits to disadvantaged communities experiencing substantial air pollution. However, the effects of these mitigation policies have rarely been evaluated with respect to their influence on disadvantaged communities. Here we assess the air pollution and environmental justice implications of California's cap-and-trade mitigation program through analysis of (1) the sources of air pollution in disadvantaged communities, (2) emissions-reduction offset usage under the cap-and-trade program, and (3) the relationship between reductions in greenhouse gas emissions and reductions in co-pollutant emissions.

Automated estimation of image quality for coronary computed tomographic angiography using machine learning.

Objectives: Our goal was to evaluate the efficacy of a fully automated method for assessing the image quality (IQ) of coronary computed tomography angiography (CCTA).

Methods: The machine learning method was trained using 75 CCTA studies by mapping features (noise, contrast, misregistration scores, and un-interpretability index) to an IQ score based on manual ground truth data. The automated method was validated on a set of 50 CCTA studies and subsequently tested on a new set of 172 CCTA studies against visual IQ scores on a 5-point Likert scale.