Randomised controlled trial of GP-led in-hospital management of homeless people ('Pathway')

The study was funded by the National Institute for Health Researc

Universal Design for Learning to support nursing students: Experiences in the Field

Higher education institutions have an increasingly diverse student population and in response have developed a range of services to support students (HEFCE 2015). Therefore, there is increased pressure to embed most support within the general university provision. As a consequence, there is a resurgence of interest in the application of universal design principles in higher education to meet the needs of individual students (AHEAD 2017). As limited international literature is available about the use of Universal Design for Learning (UDL) in nurse education; this paper will explore the application of these principles to support nursing students who have additional needs, in particular, in the practice setting

Population estimation based on multi-sensor data fusion

This research examines the utility of QuickBird imagery and Light Detection and Ranging (LiDAR) data for estimating population at the census-block level using two approaches: area-based and volume-based. Residential-building footprints are first delineated from the remote-sensing data using image segmentation and machine-learning decision-tree classification. Regression analysis is used to model the relationship between population and the area or volume of the delineated residential buildings. Both approaches result in successful performance for estimating population with high accuracy (coefficient of determination = 0.8-0.95; root-mean-square error = 10-30 people; relative root-mean-square error = 0.1-0.3). The area-based approach is slightly better than the volume-based approach because the residential areas of the study sites are generally homogeneous (i.e. single houses), and the volume-based approach is more sensitive to classification errors. The LiDAR-derived shape information such as height greatly improves population estimation compared to population estimation using only spectral data.close171

A Comparison of Error Metrics and Constraints for Multiple Endmember Spectral Mixture Analysis

Abstract Spectral matching algorithms can be used for the identification of unknown spectra based on a measure of similarity with one or more known spectra. Two popular spectral matching algorithms use different error metrics and constraints to determine the existence of a spectral match. Multiple endmember spectral mixture analysis (MESMA) is a linear mixing model that uses a root mean square error (RMSE) error metric. Spectral angle mapper (SAM) compares two spectra using a spectral angle error metric. This paper compares two endmember MESMA and SAM using a spectral library containing six land cover classes. RMSE and spectral angle for models within each land cover class were directly compared. The dependence of RMSE on the albedo of the modeled spectrum was also explored. RMSE and spectral angle were found to be closely related, although not equivalent, due to variations in the albedo of the modeled spectra. Error constraints applied to both models resulted in large differences in the number of spectral matches. Using MESMA, the number of spectra modeled within the error constraint increased as the albedo of the modeled spectra decreased. The value of the error constraint used was shown to make a much larger difference in the number of spectra modeled than the choice of spectral matching algorithm.

Estimation of Forest Fuel Load from Radar Remote Sensing

Understanding fire behavior characteristics and planning for fire management require maps showing the distribution of wildfire fuel loads at medium to fine spatial resolution across large landscapes. Radar sensors from airborne or spaceborne platforms have the potential of providing quantitative information about the forest structure and biomass components that can be readily translated to meaningful fuel load estimates for fire management. In this paper, we used multifrequency polarimetric synthetic aperture radar imagery acquired over a large area of the Yellowstone National Park (YNP) by the AIRSAR sensor, to estimate the distribution of forest biomass and canopy fuel loads. Semi-empirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, canopy fuel weight, canopy bulk density, and foliage moisture content. These estimates when compared directly to measurements made at plot and stand levels, provided more than 70% accuracy, and when partitioned into fuel load classes, provided more than 85% accuracy. Specifically, the radar generated fuel parameters were in good agreement with the field-based fuel measurements, resulting in coefficients of determination of R(sup 2) = 85 for the canopy fuel weight, R(sup 2)=.84 for canopy bulk density and R(sup 2) = 0.78 for the foliage biomass

Development of a Professional Competency Framework for Australian Sonographers – Perspectives for Developing Competencies Using a Delphi Methodology

BACKGROUND: Professional competencies are important for enhancing alignment between the needs of education, industry and health consumers, whilst describing public expectations around health professionals. The development of competency standards for the sonography profession defines the behaviours, skills and knowledge sonographers should demonstrate for each learning and experience level. OBJECTIVE: The objective of this project was to develop a set of professional competency standards for the sonography profession which described in depth the behaviours, skills and knowledge sonographers should demonstrate across multiple learning and experience levels. METHODS: Representatives of three Australian ultrasound professional associations and seven tertiary institutions involved in entry-level sonographer education in Australia formed a research team (RT). The RT recruited an expert panel that responded to six survey rounds. Using a Delphi methodology, the results and free-text comments from each previous round were fed back to participants in the subsequent survey rounds to achieve a consensus. RESULTS: The project developed a professional competency framework for sonographers, which included four major domains: detailed competency standards, sonographer knowledge, sonographer attitudes and a holistic competency matrix [https://doi.org/10.6084/m9.figshare.17148035.v2.]. CONCLUSION: The Delphi methodology is an effective way to develop professional competency standards. This paper describes the methods and challenges in developing such standards for sonographers which could be translated to other health professionals.</p

Professional Competency Framework for Sonographers

This document is the output of a research project describing a professional competency framework for sonographers. Competency frameworks organise a collection of competencies relevant to the effective performance of a particular job, job famly or functional area. A key component of competency frameworks is the idea of observable behaviours. These describe behaviours that evidence the ability to effectively perform the task, and which are underpinned by knowledge, skills and attitudes