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Transformative learning

Hilary Bradbury, Svante Lifvergren
We discuss action research healthcare as a transformative approach that continuously innovates in healthcare, attending to the "quadruple" aim. This article is shaped around a decade of evidence in Sweden. At the heart of healthcare action research is the endeavour to "learn by doing" with the participation of key stakeholders, including the patient. Experience suggests that an action research approach is particularly relevant when treating patients with chronic diseases and complex care needs. This inclusion is itself a social learning process and is key to realizing the improved outcomes...
October 20, 2016: Healthcare Management Forum
Sanna Rönkä, Anu Katainen
BACKGROUND: The non-medical use of prescription drugs is a growing phenomenon associated with increasing health-related harms. However, little is known about the drivers of this process among illicit drug users. Our aim is to show how the qualities of pharmaceutical drugs, pharmaceutical related knowledge, online communities sharing this knowledge and medical professionals mediate and transform the consumption behaviour related to pharmaceutical drugs. METHODS: The data consist of discussion threads from an online drug use forum...
October 18, 2016: International Journal on Drug Policy
Chao Zhang, Lei Du, Dacheng Tao
The techniques of random matrices have played an important role in many machine learning models. In this letter, we present a new method to study the tail inequalities for sums of random matrices. Different from other work (Ahlswede & Winter, 2002; Tropp, 2012; Hsu, Kakade, & Zhang, 2012), our tail results are based on the largest singular value (LSV) and independent of the matrix dimension. Since the LSV operation and the expectation are noncommutative, we introduce a diagonalization method to convert the LSV operation into the trace operation of an infinitely dimensional diagonal matrix...
October 20, 2016: Neural Computation
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first updated the dictionary using the method of optimal directions (MOD) and then applied a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach...
October 20, 2016: Neural Computation
Akshansh Gupta, Dhirendra Kumar
A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property...
September 3, 2016: Brain Informatics
Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu, Dong Liang
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame...
2016: BioMed Research International
Gerald Kayingo, Owais Gilani, Vasco Deon Kidd, Mary L Warner
BACKGROUND AND OBJECTIVES: The transformation of primary care (PC) training sites into patient-centered medical homes (PCMH) has implications for the education of health professionals. This study investigates the extent to which physician assistant (PA) students report learning about the PCMH model and how clinical exposure to PCMH might impact their interest in a primary care career. METHODS: An electronic survey was distributed to second-year PA students who had recently completed their PC rotation from 12 PA programs...
October 2016: Family Medicine
Susan L Taylor, Jeanne M Leffers
AIM: The aim of the research is to review all qualitative research studies related to service-learning assessment in nursing education. BACKGROUND: Recent literature reviews have examined quantitative but not qualitative research studies on service-learning assessment in nursing education. METHOD: An integrative review analyzed the results of published qualitative research of service-learning assessment. Articles included in this review were published in English in peer-reviewed journals from 1997 to 2014 and encompassed information on outcomes, assessment or evaluation, nursing education, and service-learning...
July 2016: Nursing Education Perspectives
Glòria Jodar I Solà, Joan Gené I Badia, Pilar Delgado Hito, M Antonia Campo Osaba, Jose Luís Del Val García
BACKGROUND: The concept of leadership has been studied in various disciplines and from different theoretical approaches. It is a dynamic concept that evolves over time. There are few studies in our field on managers' self-perception of their leadership style. There are no pure styles, but one or another style is generally favoured to a greater or lesser degree. In the primary health care (PHC) setting, managers' leadership style is defined as a set of attitudes, behaviours, beliefs and values...
October 12, 2016: BMC Health Services Research
Alvin Rajkomar, Sneha Lingam, Andrew G Taylor, Michael Blum, John Mongan
The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Pia-Maria Wippert, Michael Fließer
BACKGROUND: Doping presents a potential health risk for young athletes. Prevention programs are intended to prevent doping by educating athletes about banned substances. However, such programs have their limitations in practice. This led Germany to introduce the National Doping Prevention Plan (NDPP), in hopes of ameliorating the situation among young elite athletes. Two studies examined 1) the degree to which the NDPP led to improved prevention efforts in elite sport schools, and 2) the extent to which newly developed prevention activities of the national anti-doping agency (NADA) based on the NDPP have improved knowledge among young athletes within elite sports schools...
October 10, 2016: Substance Abuse Treatment, Prevention, and Policy
Nikola K Kasabov, Maryam Gholami Doborjeh, Zohreh Gholami Doborjeh
This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation...
October 6, 2016: IEEE Transactions on Neural Networks and Learning Systems
Xiao Luo, Tiantian Qiu, Yunlu Jia, Peiyu Huang, Xiaojun Xu, Xinfeng Yu, Zhujing Shen, Yerfan Jiaerken, Xiaojun Guan, Jiong Zhou, Minming Zhang
Apolipoprotein E (APOE) ε4 allele is the best established genetic risk factor for sporadic Alzheimer's disease (AD). However, there is a need to understand the effects of this genotype on the brain by simultaneously assessing intrinsic brain network and cerebral spinal fluid (CSF) biomarkers changes in healthy older ε4 carriers. Thirteen cognitively intact, elderly APOE ε4 carriers and 22 ε3 homozygotes were included in the present study. Eigenvector centrality mapping (ECM) was used to identify brain network hub organization based on resting-state functional MRI (rsfMRI)...
October 6, 2016: Brain Imaging and Behavior
Sierra Eisen, Angeline S Lillard
Children today regularly interact with touchscreen devices (Rideout, 2013) and thousands of "educational" mobile applications are marketed to them (Shuler, 2012). Understanding children's own ideas about optimal learning has important implications for education, which is being transformed by electronic mobile devices, yet we know little about how children think about such devices, including what children think touchscreens are useful for. Based on a prior result that children prefer a book over a touchscreen for learning about dogs, the present study explored how children view touchscreens versus books for learning an array of different types of information...
2016: Frontiers in Psychology
Honesty Kim, Lukas Cyrill Gerber, Daniel Chiu, Seung Ah Lee, Nate J Cira, Sherwin Yuyang Xia, Ingmar H Riedel-Kruse
For centuries, observational microscopy has greatly facilitated biology education, but we still cannot easily and playfully interact with the microscopic world we see. We therefore developed the LudusScope, an accessible, interactive do-it-yourself smartphone microscopy platform that promotes exploratory stimulation and observation of microscopic organisms, in a design that combines the educational modalities of build, play, and inquire. The LudusScope's touchscreen and joystick allow the selection and stimulation of phototactic microorganisms such as Euglena gracilis with light...
2016: PloS One
Chengzhi Wu, Bo Qi, Chunlin Chen, Daoyi Dong
Robust control design for quantum unitary transformations has been recognized as a fundamental and challenging task in the development of quantum information processing due to unavoidable decoherence or operational errors in the experimental implementation of quantum operations. In this paper, we extend the systematic methodology of sampling-based learning control (SLC) approach with a gradient flow algorithm for the design of robust quantum unitary transformations. The SLC approach first uses a "training" process to find an optimal control strategy robust against certain ranges of uncertainties...
September 29, 2016: IEEE Transactions on Cybernetics
Ruoqing Zhu, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, Hongyu Zhao
We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a high-dimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules...
October 4, 2016: Biometrics
Linda R Watson
In the next decade, professionals in communication sciences and disorders will encounter a wealth of needs, opportunities, and challenges in research and practice related to autism spectrum disorder. What lies ahead will reflect both transformations of and continuities with past perspectives (psychodynamic, biological, and learning theory). Among our largest challenges as individuals and as a discipline will be to determine the most important needs to address and the most productive opportunities to seize. Interprofessional collaboration, community engagement, and partnerships among researchers, practitioners, and community stakeholder are all strategies that can better guide our selection of priorities...
November 2016: Seminars in Speech and Language
Christian Hilbe, Kristin Hagel, Manfred Milinski
Direct reciprocity is a major mechanism for the evolution of cooperation. Several classical studies have suggested that humans should quickly learn to adopt reciprocal strategies to establish mutual cooperation in repeated interactions. On the other hand, the recently discovered theory of ZD strategies has found that subjects who use extortionate strategies are able to exploit and subdue cooperators. Although such extortioners have been predicted to succeed in any population of adaptive opponents, theoretical follow-up studies questioned whether extortion can evolve in reality...
2016: PloS One
Kabir Sheikh, Mukund Uplekar
BACKGROUND: The unregulated availability and irrational use of tuberculosis (TB) medicines is a major issue of public health concern globally. Governments of many low- and middle-income countries (LMICs) have committed to regulating the quality and availability of TB medicines, but with variable success. Regulation of TB medicines remains an intractable challenge in many settings, but the reasons for this are poorly understood. The objective of this paper is to elaborate processes of regulation of quality and availability of TB medicines in three LMICs - India, Tanzania, and Zambia - and to understand the factors that constrain and enable these processes...
March 9, 2016: International Journal of Health Policy and Management
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