Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

Purpose: Whether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown. Methods: In this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis. Results: Of the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0–1.00) and 85.9% (75.4–92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20–2.92) or receiving a written TLD (HR 2.32, CI 1.11–4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former. Conclusion: Enhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life

How generalizable is the inverse relationship between social class and emotion perception?

Compared to individuals in lower positions of power, higher-power individuals are theorized to be less motivated to attend to social cues. In support of this theory, previous research has consistently documented negative correlations between social class and emotion perception. Prior studies, however, were limited by the size and diversity of the participant samples as well as the systematicity with which social class and emotion perception were operationalized. Here, we examine the generalizability of prior research across 10,000+ total participants. In an initial modest sample, (n = 179), Study 1 partially replicated past results: emotion identification correlated negativity with subjective social class (β = -0.15, 95% CI = [-0.28,-0.02]) and one of two objective social class measures (participant education β = -0.15, 95% CI = [-0.03,-0.01]). Studies 2-4 followed up on Study 1's mixed results for objective social class in three much larger samples. These results diverged from past literature. In Study 2, complex emotion identification correlated non-significantly with participant education (β = 0.02, p = 0.25; 95% CI = [-0.01, 0.05], n = 2,726), positively with childhood family income (β = 0.03, 95% CI = [0.01,0.06], n = 4,312), and positively with parental education (β = 0.06, 95% CI = [0.04,0.09], n = 4,225). In Study 3, basic emotion identification correlated positively with participant education (β = 0.05, 95% CI = [0.02, 0.09]), n = 2,564). In Study 4, basic emotion discrimination correlated positively with participant education (β = 0.09, 95% CI = [0.05,0.13], n = 2,079), positively with parental education (β = 0.06, 95% CI = [0.02,0.09], n = 3,225), and non-significantly with childhood family income (β = 0.2, 95% CI = [0.01,0.07], n = 3,272). Results remained similar when restricting analyses to U.S.-based participants. Taken together, these findings suggest that previously reported negative correlations between emotion perception and social class may generalize poorly past select samples and/or subjective measures of social class. Data from the three large web-based samples used in Studies 2-4 are available at osf.io/jf7r3 as normative datasets and to support future investigations of these and other research questions

Challenges in Building an End-to-End System for Acquisition, Management, and Integration of Diverse Data From Sensor Networks in Watersheds: Lessons From a Mountainous Community Observatory in East River, Colorado

The U.S. Department of Energy's Watershed Function Scientific Focus Area (SFA), centered in the East River, Colorado, generates diverse datasets including hydrological, geological, geochemical, geophysical, ecological, microbiological and remote sensing data. The project has deployed extensive field infrastructure involving hundreds of sensors that measure highly diverse phenomena (e.g. stream and groundwater hydrology, water quality, soil moisture, weather) across the watershed. Data from the sensor network are telemetered and automatically ingested into a queryable database. The data are subsequently quality checked, integrated with the United States Geological Survey's stream monitoring network using a custom data integration broker, and published to a portal with interactive visualizations. The resulting data products are used in a variety of scientific modeling and analytical efforts. This paper describes the SFA's end-to-end infrastructure and services that support the generation of integrated datasets from a watershed sensor network. The development and maintenance of this infrastructure, presents a suite of challenges from practical field logistics to complex data processing, which are addressed through various solutions. In particular, the SFA adopts a holistic view for data collection, assessment and integration, which dramatically improves the products generated, and enables a co-design approach wherein data collection is informed by model results and vice-versa.U.S. Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; WatershedFunction Scientific Focus Area - U.S. Department of Energy, Office of Science, Office of Biological, and Environmental ResearchUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; National Energy Research Scientific Computing Center (NERSC), U.S. Department of Energy Office of Science User FacilityUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) [DE-AC02-05CH11231]; [DE-SC0009732]; [DE-SC0018447]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]