Use of cognitive interviewing in the development of a Spanish language genetic knowledge instrument

Study purpose. Genetic advances are significantly impacting healthcare, yet recent studies of ethnic group participation in genetic services demonstrate low utilization rates by Latinos. Limited genetic knowledge is a major barrier. The purpose of this study was to field test items in a Spanish-language instrument that will be used to measure genetic knowledge relevant to type 2 diabetes among members of the ethnically heterogeneous U.S. Latino community. Accurate genetic knowledge measurement can provide the foundation for interventions to enhance genetic service utilization. ^ Design. Three waves of cognitive interviews were conducted in Spanish to field test 44 instrument items Thirty-six Latinos, with 12 persons representative of Mexican, Central and South American, and Cuban heritage participated, including 7 males and 29 females between 22 and 60 years of age; 17 participants had 12 years or less of education. ^ Methods. Text narratives from transcriptions of audiotaped interviews were qualitatively analyzed using a coding strategy to indicate potential sources of response error. Through an iterative process of instrument refinement, codes that emerged from the data were used to guide item revisions at the conclusion of each phase; revised items were examined in subsequent interview waves. ^ Results. Inter-cultural and cross-cultural themes associated with difficulties in interpretation and grammatical structuring of items were identified; difficulties associated with comprehension reflected variations in educational level. Of the original 44 items, 32 were retained, 89% of which were revised. Six additional items reflective of cultural knowledge were constructed, resulting in a 38-item instrument. ^ Conclusions. Use of cognitive interviewing provided a valuable tool for detecting both potential sources of response error and cultural variations in these sources. Analysis of interview data guided successive instrument revisions leading to improved item interpretability and comprehension. Although testing in a larger sample will be essential to test validity and reliability, the outcome of field testing suggests initial content validity of a Spanish-language instrument to measure genetic knowledge relative to type 2 diabetes. ^ Keywords. Latinos, genetic knowledge, instrument development, cognitive interviewing ^

UNDERSTANDING NURSE CREATED COGNITIVE ARTIFACTS: PERSONALLY-CREATED-COGNITIVE-ARTIFACTS AS EXTERNAL REPRESENTATIONS OF DISTRIBUTED COGNITION

Manuscript 1: “Conceptual Analysis: Externalizing Nursing Knowledge” We use concept analysis to establish that the report tool nurses prepare, carry, reference, amend, and use as a temporary data repository are examples of cognitive artifacts. This tool, integrally woven throughout the work and practice of nurses, is important to cognition and clinical decision-making. Establishing the tool as a cognitive artifact will support new dimensions of study. Such studies can characterize how this report tool supports cognition, internal representation of knowledge and skills, and external representation of knowledge of the nurse. Manuscript 2: “Research Methods: Exploring Cognitive Work” The purpose of this paper is to describe a complex, cross-sectional, multi-method approach to study of personal cognitive artifacts in the clinical environment. The complex data arrays present in these cognitive artifacts warrant the use of multiple methods of data collection. Use of a less robust research design may result in an incomplete understanding of the meaning, value, content, and relationships between personal cognitive artifacts in the clinical environment and the cognitive work of the user. Manuscript 3: “Making the Cognitive Work of Registered Nurses Visible” Purpose: Knowledge representations and structures are created and used by registered nurses to guide patient care. Understanding is limited regarding how these knowledge representations, or cognitive artifacts, contribute to working memory, prioritization, organization, cognition, and decision-making. The purpose of this study was to identify and characterize the role a specific cognitive artifact knowledge representation and structure as it contributed to the cognitive work of the registered nurse. Methods: Data collection was completed, using qualitative research methods, by shadowing and interviewing 25 registered nurses. Data analysis employed triangulation and iterative analytic processes. Results: Nurse cognitive artifacts support recall, data evaluation, decision-making, organization, and prioritization. These cognitive artifacts demonstrated spatial, longitudinal, chronologic, visual, and personal cues to support the cognitive work of nurses. Conclusions: Nurse cognitive artifacts are an important adjunct to the cognitive work of nurses, and directly support patient care. Nurses need to be able to configure their cognitive artifact in ways that are meaningful and support their internal knowledge representations.

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Efficiency improvement of HF communications using cognitive radio principles

Cognitive Radio principles can be applied to HF communications to make a more efficient use of the extremely scarce spectrum. In this contribution we focus on analyzing the usage of the available channels done by the legacy users, which are regarded as primary users since they are allowed to transmit without resorting any smart procedure, and consider the possibilities for our stations -over the HFDVL (HF Data+Voice Link) architecture- to participate as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes, and is trained with real measurements from the 14 MHz band.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Minimally invasive surgical video analysis: a powerful tool for surgical training and navigation

Analysis of minimally invasive surgical videos is a powerful tool to drive new solutions for achieving reproducible training programs, objective and transparent assessment systems and navigation tools to assist surgeons and improve patient safety. This paper presents how video analysis contributes to the development of new cognitive and motor training and assessment programs as well as new paradigms for image-guided surgery.

Minimally invasive surgical video analysis: a powerful tool for surgical training and navigation

Analysis of minimally invasive surgical videos is a powerful tool to drive new solutions for achieving reproducible training programs, objective and transparent assessment systems and navigation tools to assist surgeons and improve patient safety. This paper presents how video analysis contributes to the development of new cognitive and motor training and assessment programs as well as new paradigms for image-guided surgery.

Cognitive test-bed for wireless sensor networks

Cognitive Wireless Sensor Networks are an emerging technology with a vast potential to avoid traditional wireless problems such as reliability, interferences and spectrum scarcity in Wireless Sensor Networks. Cognitive Wireless Sensor Networks test-beds are an important tool for future developments, protocol strategy testing and algorithm optimization in real scenarios. A new cognitive test-bed for Cognitive Wireless Sensor Networks is presented in this paper. This work in progress includes both the design of a cognitive simulator for networks with a high number of nodes and the implementation of a new platform with three wireless interfaces and a cognitive software for extracting real data. Finally, as a future work, a remote programmable system and the planning for the physical deployment of the nodes at the university building is presented.

Cognitive strategies for reducing energy consumption in wireless sensor networks

El consumo energético de las Redes de Sensores Inalámbricas (WSNs por sus siglas en inglés) es un problema histórico que ha sido abordado desde diferentes niveles y visiones, ya que no solo afecta a la propia supervivencia de la red sino que el creciente uso de dispositivos inteligentes y el nuevo paradigma del Internet de las Cosas hace que las WSNs tengan cada vez una mayor influencia en la huella energética. Debido a la tendencia al alza en el uso de estas redes se añade un nuevo problema, la saturación espectral. Las WSNs operan habitualmente en bandas sin licencia como son las bandas Industrial, Científica y Médica (ISM por sus siglas en inglés). Estas bandas se comparten con otro tipo de redes como Wi-Fi o Bluetooth cuyo uso ha crecido exponencialmente en los últimos años. Para abordar este problema aparece el paradigma de la Radio Cognitiva (CR), una tecnología que permite el acceso oportunista al espectro. La introducción de capacidades cognitivas en las WSNs no solo permite optimizar su eficiencia espectral sino que también tiene un impacto positivo en parámetros como la calidad de servicio, la seguridad o el consumo energético. Sin embargo, por otra parte, este nuevo paradigma plantea algunos retos relacionados con el consumo energético. Concretamente, el sensado del espectro, la colaboración entre los nodos (que requiere comunicación adicional) y el cambio en los parámetros de transmisión aumentan el consumo respecto a las WSN clásicas. Teniendo en cuenta que la investigación en el campo del consumo energético ha sido ampliamente abordada puesto que se trata de una de sus principales limitaciones, asumimos que las nuevas estrategias deben surgir de las nuevas capacidades añadidas por las redes cognitivas. Por otro lado, a la hora de diseñar estrategias de optimización para CWSN hay que tener muy presentes las limitaciones de recursos de estas redes en cuanto a memoria, computación y consumo energético de los nodos. En esta tesis doctoral proponemos dos estrategias de reducción de consumo energético en CWSNs basadas en tres pilares fundamentales. El primero son las capacidades cognitivas añadidas a las WSNs que proporcionan la posibilidad de adaptar los parámetros de transmisión en función del espectro disponible. La segunda es la colaboración, como característica intrínseca de las CWSNs. Finalmente, el tercer pilar de este trabajo es teoría de juegos como algoritmo de soporte a la decisión, ampliamente utilizado en WSNs debido a su simplicidad. Como primer aporte de la tesis se presenta un análisis completo de las posibilidades introducidas por la radio cognitiva en materia de reducción de consumo para WSNs. Gracias a las conclusiones extraídas de este análisis, se han planteado las hipótesis de esta tesis relacionadas con la validez de usar capacidades cognitivas como herramienta para la reducción de consumo en CWSNs. Una vez presentada las hipótesis, pasamos a desarrollar las principales contribuciones de la tesis: las dos estrategias diseñadas para reducción de consumo basadas en teoría de juegos y CR. La primera de ellas hace uso de un juego no cooperativo que se juega mediante pares de jugadores. En la segunda estrategia, aunque el juego continúa siendo no cooperativo, se añade el concepto de colaboración. Para cada una de las estrategias se presenta el modelo del juego, el análisis formal de equilibrios y óptimos y la descripción de la estrategia completa donde se incluye la interacción entre nodos. Con el propósito de probar las estrategias mediante simulación e implementación en dispositivos reales hemos desarrollado un marco de pruebas compuesto por un simulador cognitivo y un banco de pruebas formado por nodos cognitivos capaces de comunicarse en tres bandas ISM desarrollados en el B105 Lab. Este marco de pruebas constituye otra de las aportaciones de la tesis que permitirá el avance en la investigación en el área de las CWSNs. Finalmente, se presentan y discuten los resultados derivados de la prueba de las estrategias desarrolladas. La primera estrategia proporciona ahorros de energía mayores al 65% comparados con una WSN sin capacidades cognitivas y alrededor del 25% si la comparamos con una estrategia cognitiva basada en el sensado periódico del espectro para el cambio de canal de acuerdo a un nivel de ruido fijado. Este algoritmo se comporta de forma similar independientemente del nivel de ruido siempre que éste sea espacialmente uniformemente. Esta estrategia, a pesar de su sencillez, nos asegura el comportamiento óptimo en cuanto a consumo energético debido a la utilización de teoría de juegos en la fase de diseño del comportamiento de los nodos. La estrategia colaborativa presenta mejoras respecto a la anterior en términos de protección frente al ruido en escenarios de ruido más complejos donde aporta una mejora del 50% comparada con la estrategia anterior. ABSTRACT Energy consumption in Wireless Sensor Networks (WSNs) is a known historical problem that has been addressed from different areas and on many levels. But this problem should not only be approached from the point of view of their own efficiency for survival. A major portion of communication traffic has migrated to mobile networks and systems. The increased use of smart devices and the introduction of the Internet of Things (IoT) give WSNs a great influence on the carbon footprint. Thus, optimizing the energy consumption of wireless networks could reduce their environmental impact considerably. In recent years, another problem has been added to the equation: spectrum saturation. Wireless Sensor Networks usually operate in unlicensed spectrum bands such as Industrial, Scientific, and Medical (ISM) bands shared with other networks (mainly Wi-Fi and Bluetooth). To address the efficient spectrum utilization problem, Cognitive Radio (CR) has emerged as the key technology that enables opportunistic access to the spectrum. Therefore, the introduction of cognitive capabilities to WSNs allows optimizing their spectral occupation. Cognitive Wireless Sensor Networks (CWSNs) do not only increase the reliability of communications, but they also have a positive impact on parameters such as the Quality of Service (QoS), network security, or energy consumption. These new opportunities introduced by CWSNs unveil a wide field in the energy consumption research area. However, this also implies some challenges. Specifically, the spectrum sensing stage, collaboration among devices (which requires extra communication), and changes in the transmission parameters increase the total energy consumption of the network. When designing CWSN optimization strategies, the fact that WSN nodes are very limited in terms of memory, computational power, or energy consumption has to be considered. Thus, light strategies that require a low computing capacity must be found. Since the field of energy conservation in WSNs has been widely explored, we assume that new strategies could emerge from the new opportunities presented by cognitive networks. In this PhD Thesis, we present two strategies for energy consumption reduction in CWSNs supported by three main pillars. The first pillar is that cognitive capabilities added to the WSN provide the ability to change the transmission parameters according to the spectrum. The second pillar is that the ability to collaborate is a basic characteristic of CWSNs. Finally, the third pillar for this work is the game theory as a decision-making algorithm, which has been widely used in WSNs due to its lightness and simplicity that make it valid to operate in CWSNs. For the development of these strategies, a complete analysis of the possibilities is first carried out by incorporating the cognitive abilities into the network. Once this analysis has been performed, we expose the hypotheses of this thesis related to the use of cognitive capabilities as a useful tool to reduce energy consumption in CWSNs. Once the analyses are exposed, we present the main contribution of this thesis: the two designed strategies for energy consumption reduction based on game theory and cognitive capabilities. The first one is based on a non-cooperative game played between two players in a simple and selfish way. In the second strategy, the concept of collaboration is introduced. Despite the fact that the game used is also a non-cooperative game, the decisions are taken through collaboration. For each strategy, we present the modeled game, the formal analysis of equilibrium and optimum, and the complete strategy describing the interaction between nodes. In order to test the strategies through simulation and implementation in real devices, we have developed a CWSN framework composed by a CWSN simulator based on Castalia and a testbed based on CWSN nodes able to communicate in three different ISM bands. We present and discuss the results derived by the energy optimization strategies. The first strategy brings energy improvement rates of over 65% compared to WSN without cognitive techniques. It also brings energy improvement rates of over 25% compared with sensing strategies for changing channels based on a decision threshold. We have also seen that the algorithm behaves similarly even with significant variations in the level of noise while working in a uniform noise scenario. The collaborative strategy presents improvements respecting the previous strategy in terms of noise protection when the noise scheme is more complex where this strategy shows improvement rates of over 50%.

Illusory memories: A cognitive neuroscience analysis

Memory illusions and distortions have long been of interest to psychology researchers studying memory, but neuropsychologists and neuroscientists have paid relatively little attention to them. This article attempts to lay the foundation for a cognitive neuroscience analysis of memory illusions and distortions by reviewing relevant evidence from a patient with a right frontal lobe lesion, patients with amnesia produced by damage to the medial temporal lobes, normal aging, and healthy young volunteers studied with functional neuroimaging techniques. Particular attention is paid to the contrasting roles of prefrontal cortex and medial temporal lobe structures in accurate and illusory remembering. Converging evidence suggests that the study of illusory memories can provide a useful tool for delineating the brain processes and systems involved in constructive aspects of remembering.

Using auditory hallucinations as a therapeutic tool.

This paper integrates psychodynamic and cognitive theories understanding of schizophrenia and the function of auditory hallucinations to propose five principles for the successful treatment of auditory hallucinations within schizophrenia; connection, education, collaboration, empowerment, and independence. Pharmacological and biological research is acknowledged and integrated into psychological treatment using case information based on an autobiographical novel by a person diagnosed with schizophrenia.

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

Proyecto FIRST (Flexible Interactive Reading Support Tool): desarrollo de una herramienta para ayudar a personas con autismo mediante la simplificación de textos

El Trastorno de Espectro Autista (TEA) es un trastorno que impide el correcto desarrollo de funciones cognitivas, habilidades sociales y comunicativas en las personas. Un porcentaje significativo de personas con autismo presentan además dificultades en la comprensión lectora. El proyecto europeo FIRST está orientado a desarrollar una herramienta multilingüe llamada Open Book que utiliza Tecnologías del Lenguaje Humano para identificar obstáculos que dificultan la comprensión lectora de un documento. La herramienta ayuda a cuidadores y personas con autismo transformando documentos escritos a un formato más sencillo mediante la eliminación de dichos obstáculos identificados en el texto. En este artículo se presenta el proyecto FIRST así como la herramienta desarrollada Open Book.

Is video-case an efficient tool to complement the practical learning in real contexts? Effects on satisfaction and performance in Social Work Degree students

Este documento es un artículo inédito que ha sido aceptado para su publicación. Como un servicio a sus autores y lectores, Alternativas. Cuadernos de trabajo social proporciona online esta edición preliminar. El manuscrito puede sufrir alteraciones tras la edición y corrección de pruebas, antes de su publicación definitiva. Los posibles cambios no afectarán en ningún caso a la información contenida en esta hoja, ni a lo esencial del contenido del artículo.