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INTRODUCTION: Sustainable approaches to support care coordination and symptom management needs of critically ill adults living with multimorbidity are needed to combat the challenges and complexity that multimorbidity presents. The study aims to test the feasibility of the Care cOORDInatioN And sympTom managEment (COORDINATE) intervention to improve health outcomes of adults living with multimorbidity. METHODS AND ANALYSIS: A multicomponent nurse-driven intervention was developed using experience-based co-design and human-centred design. Inclusion criteria include (1) age 55 years and older, (2) admitted to an intermediate care unit, (3) presence of two or more chronic health conditions and (4) signed informed consent. Data collection will occur at baseline (time of recruitment predischarge) and 6 weeks and 3 months following hospital discharge. Outcome of interest from this feasibility study is to evaluate the financial, technical and logistic feasibility of a full-scale study including data collection and protocol adherence. Additionally, Cohen's d effect sizes for the change in outcomes over time will be computed to establish power calculations required for a full-scale study. The protocol was prepared in accordance with Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist. ETHICS AND DISSEMINATION: The study has been reviewed and approved by the Institutional Review Board of Johns Hopkins Medical Institutions. Given the success of this feasibility study, the potential for the COORDINATE intervention to decrease the symptom burden and improve participant quality of life among critically ill people with multimorbidity will be tested in a full-scale study, and findings will be actively disseminated. TRIAL REGISTRATION NUMBER: NCT05985044.

AIM: Describe the strategy, efficacy and preferred mechanisms of training used to rapidly upskill intermediate care nursing staff to provide critical care during the COVID-19 pandemic. DESIGN: Descriptive study. METHODS: The strategy used from March through December 2020 to upskill nurses in an intermediate care unit to administer critical care upon rapid conversion of the intermediate care unit to an intensive care unit for coronavirus disease 2019 is described. Training and education included paired staffing models, interdisciplinary education, skills days and self-directed learning. Nurses engaged in this upskilling process were surveyed to evaluate their confidence in new critical care competencies and educational preferences. RESULTS: Of 38 intermediate care nurses, 35 completed training and began independent intensive care practice. Nursing confidence in critical care competencies increased steadily. Nurses demonstrated the greatest preference for peer education models, particularly those incorporating the hospital's pre-existing medical intensive care nurses. PATIENT AND PUBLIC CONTRIBUTIONS: No patient or public contributions were made to this manuscript.

Fiscal constraints, an ageing populations and the increasing burden of chronic conditions are stressing health systems internationally. Nurses are the linchpin of effective healthcare delivery and their success is dependent on adequate staffing models, which must align knowledge, skills and competencies with workload. Objectives: To compare measures of nursing workload in adult inpatient settings. Design, data sources and review method: A review of published studies characterising nursing workload measures was undertaken. Databases-PubMed and CINHAL-were used to identify published studies. A description of the psychometric properties of each measure and its use in an inpatient setting was required for inclusion. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to guide and report the review. Results: Of the 1,422 studies identified, 15 met the inclusion criteria. Nursing workload was measured in the intermediate care unit (n = 6), overall hospital (n = 7), emergency department (n = 1) and burn unit (n = 1) settings and also by mailed survey (n = 1). Eleven different workload measures were identified. The National Aeronautics and Space Administration Task Load Index (n = 3), Therapeutic Intervention Scoring System (n = 3) and Nursing Activities Score (n = 2) were the most common nursing workload measures identified with reported psychometric properties. Conclusion: Researchers, clinicians and hospital administrators should carefully identify and assess the psychometric properties of nursing workload measures before using these in routine practice. Relevance to clinical practice: Gaining a consensus on effective nursing workload measures is a crucial step in designing appropriate staffing models and policies, improving nurse productivity and well-being, as well as enhancing patient health outcomes in inpatient settings.

Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub-models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf-scale empirical Rleaf,m models are undetermined. Here we combine alternate well-known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI-TCN patterns with predictions of optimal LAI-TCN. We recommend caution in using leaf-scale empirical models for components of ESMs at canopy-scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI-TCN space to understand and mitigate these risks.

Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub-models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf-scale empirical Rleaf,m models are undetermined. Here we combine alternate well-known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI-TCN patterns with predictions of optimal LAI-TCN. We recommend caution in using leaf-scale empirical models for components of ESMs at canopy-scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI-TCN space to understand and mitigate these risks.

Motivation: The Tundra Trait Team (TTT) database includes field‐based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade‐offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained: The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain: Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub‐Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain: All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement: Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format: csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.

Motivation: The Tundra Trait Team (TTT) database includes field-based measurements of key traits related to plant form and function at multiple sites across the tundra biome. This dataset can be used to address theoretical questions about plant strategy and trade-offs, trait–environment relationships and environmental filtering, and trait variation across spatial scales, to validate satellite data, and to inform Earth system model parameters. Main types of variable contained: The database contains 91,970 measurements of 18 plant traits. The most frequently measured traits (> 1,000 observations each) include plant height, leaf area, specific leaf area, leaf fresh and dry mass, leaf dry matter content, leaf nitrogen, carbon and phosphorus content, leaf C:N and N:P, seed mass, and stem specific density. Spatial location and grain: Measurements were collected in tundra habitats in both the Northern and Southern Hemispheres, including Arctic sites in Alaska, Canada, Greenland, Fennoscandia and Siberia, alpine sites in the European Alps, Colorado Rockies, Caucasus, Ural Mountains, Pyrenees, Australian Alps, and Central Otago Mountains (New Zealand), and sub-Antarctic Marion Island. More than 99% of observations are georeferenced. Time period and grain: All data were collected between 1964 and 2018. A small number of sites have repeated trait measurements at two or more time periods. Major taxa and level of measurement: Trait measurements were made on 978 terrestrial vascular plant species growing in tundra habitats. Most observations are on individuals (86%), while the remainder represent plot or site means or maximums per species. Software format: csv file and GitHub repository with data cleaning scripts in R; contribution to TRY plant trait database (www.try-db.org) to be included in the next version release.

Dissolved organic carbon (DOC) is typically the predominant form of carbon exported from headwater streams, it therefore represents a major carbon export from Arctic catchments. The projected deepening of thaw depth in permafrost regions, due to an increase in air temperature, may have a significant effect on the amount of DOC exported from these systems. However, quantification of the impacts of climate driven changes on DOC export are still highly uncertain. Understanding the processes controlling DOC export is therefore crucial in predicting the potential impact of projected environmental changes. The controls of DOC production and transport are heavily influenced by soil and vegetation, which are highly variable across the landscape. To completely understand these systems information regarding spatial variability of plants, soils and thaw depths must be taken into account. In this study sub-weekly sampling of DOC was undertaken throughout 2014 in a headwater (<1 km2) catchment in the Northwest Territories, Canada. Spatial surveys of soil properties, active thaw depth and normalised difference vegetation index (NDVI) were collected and used in conjunction with conservative stable water isotopes tracers and major ions to understand sources, flow pathways and timing of DOC exports from the catchment. Stable isotope tracers act as fingerprints of water allowing sources and pathways to be assessed. Observations reveal changing DOC concentrations throughout the season as the active layer deepens and the connectivity of the soils to the stream network throughout the catchment increases. Linking the DOC data with the conservative tracer response improves the identification of carbon pathways and fluxes from the soils; preliminary analysis indicates DOC is being delivered via deeper more mineral soils later in the season. The results indicate that the active layer depth has a strong influence on the amount of DOC exported from the system, independent of the amount of car

The aquatic pathway is increasingly being recognized as an important component of landscape scale greenhouse gas (GHG) budgets. Due to low temperatures and short residence times limiting in-stream production in northern headwater catchments, much of the exported carbon is likely to be allochthonous, transported via throughflow to the surface drainage system. Identifying sources and primary flow pathways is therefore essential in understanding and predicting changes in the aquatic flux magnitude. Arctic landscapes are now widely recognised as being particularly vulnerable to climate driven changes. The HYDRA project (“Permafrost catchments in transition: hydrological controls on carbon cycling and greenhouse gas budgets”) aims to understand the fundamental role that hydrological processes play in regulating landscape-scale carbon fluxes, and predict how changes in vegetation and active layer depth in permafrost environments influence the delivery and export of aquatic carbon. In this study we present aquatic concentrations and fluxes of carbon and GHG species collected across two field seasons (2013, 2014) from an arctic headwater catchment in northern Canada. Measured species include dissolved organic (DOC) and inorganic carbon (DIC), CO2, CH4 and N2O. Measurements were made across a range of freshwater types within the tundra landscape, including lakes, ice-wedge polygons, and the ‘Siksik’ stream which drains the (c.a. 1 km2) primary study catchment. A nested sub-catchment approach was used along the ‘Siksik’ stream; ‘snapshot’ sampling of eight points along the stream length allowed specific vegetation communities to be targeted to assess individually their contribution to aquatic export. A combination of stable isotopes and major ion concentrations measured at each sampling point provide additional information to trace source areas and flow paths within the main study catchment. Catchment scale evasion and downstream export were calculated and an initial comparis

The European Soil Erosion Model is a process-based computer model for simulating soil erosion and runoff from fields and small catchments for single rainstorm events. The soil erosion processes are described by a series of algorithms linked together to form the model. To make the computer model more accessible to users, a graphical user interface is currently under development. This paper describes some of the main interface features, including catchment visual representation, easy access to input data, and element-wise dynamic output of simulation results.

This paper describes an event-based, process-oriented, ephemeral gully erosion model for predicting soil erosion by concentrated overland flow from fields and small catchments. It is designed as a new component for the European Soil Erosion Model (EUROSEM). The ephemeral gully model uses a transport capacity approach to simulate simultaneous soil detachment, transport and deposition. Discharge and sediment concentration are calculated by solution of the dynamic mass balance equations for water and sediment respectively. The model accounts for changes in gully geometry due to lateral erosion, bed degradation, bank failure, and sediment deposition. The bank failure sub-model assumes cantilever failure dominates and is related to cantilever width, bank geometry, and the influence of rapid drawdown. Key features of the model are outlined together with plans for further research.