Suscetibilidade de operárias e larvas de abelhas sociais em relação à ricinina

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Performance of a Brazilian population on the test of functional health literacy in adults

OBJECTIVE: To analyze the scoring obtained by an instrument, which evaluates the ability to read and understand items in the health care setting, according to education and age. METHODS: The short version of the Test of Functional Health Literacy in Adults was administered to 312 healthy participants of different ages and years of schooling. The study was conducted between 2006 and 2007, in the city of São Paulo, Southeastern Brazil. The test includes actual materials such as pill bottles and appointment slips and measures reading comprehension, assessing the ability to read and correctly pronounce a list of words and understand both prose passages and numerical information. Pearson partial correlations and a multiple regression model were used to verify the association between its scores and education and age. RESULTS: The mean age of the sample was 47.3 years(sd=16.8) and the mean education was 9.7 years(sd=5; range: 1 - 17). A total of 32.4% of the sample showed literacy/numeracy deficits, scoring in the inadequate and marginal functional health literacy ranges. Among the elderly (65 years or older) this rate increased to 51.6%. There was a positive correlation between schooling and scores (r=0.74; p<0.01) and a negative correlation between age and the scores (r=-0.259; p<0.01). The correlation between the scores and age was not significant when the effects of education were held constant (rp=-0.031, p=0.584). A significant association (B=3.877, Beta =0.733; p<0.001) was found between schooling and scores. Age was not a significant predictor in this model (B=-0.035, Beta=-0.22; p=0.584). CONCLUSIONS: The short version of the Test of Functional Health Literacy in Adults was a suitable tool to assess health literacy in the study population. The high number of individuals classified as functional illiterates in this test highlights the importance of special assistance to help them properly understand directions for healthcare.

Diagnosis of latent tuberculosis in patients with systemic lupus erythematosus: T.SPOT.TB versus tuberculin skin test.

Early studies in patients with systemic lupus erythematosus (SLE) reported increased incidence of tuberculosis. The tuberculin skin test (TST) is the technique of choice to detect latent tuberculosis infection (LTBI) but has several limitations. OBJECTIVES We compared TST and the newer T.SPOT.TB test to diagnose LTBI in SLE patients. METHODS In this observational cohort study conducted between August 2009 and February 2012, we recruited 92 patients from those attending the SLE clinic of our university hospital. Data recorded were epidemiological and sociodemographic characteristics. Laboratory analyses included TST and T.SPOT.TB tests. RESULTS Of the patients studied, 92% were women with an average age of 42.7 years. Overall, the degree of correlation between the two tests was low (Kappa index = 0.324) but was better in patients not receiving corticosteroids (CTC)/immunosuppressive (IS) therapy (Kappa = 0.436) and in those receiving hydroxychloroquine (Kappa = 0.473). While TST results were adversely affected by those receiving CTC and/or IS drugs (P = 0.021), the T.SPOT.TB results were not. CONCLUSION Although the TST test remains a useful tool for diagnosing LTBI in SLE patients, the T.SPOT.TB test is perhaps better employed when the patient is receiving CTC and/or IS drugs.

A combination of ascorbic acid and α-tocopherol to test the effectiveness and safety in the fragile X syndrome: study protocol for a phase II, randomized, placebo-controlled trial.

BACKGROUND Fragile X syndrome (FXS) is an inherited neurodevelopmental condition characterised by behavioural, learning disabilities, physical and neurological symptoms. In addition, an important degree of comorbidity with autism is also present. Considered a rare disorder affecting both genders, it first becomes apparent during childhood with displays of language delay and behavioural symptoms.Main aim: To show whether the combination of 10 mg/kg/day of ascorbic acid (vitamin C) and 10 mg/kg/day of α-tocopherol (vitamin E) reduces FXS symptoms among male patients ages 6 to 18 years compared to placebo treatment, as measured on the standardized rating scales at baseline, and after 12 and 24 weeks of treatment.Secondary aims: To assess the safety of the treatment. To describe behavioural and cognitive changes revealed by the Developmental Behaviour Checklist Short Form (DBC-P24) and the Wechsler Intelligence Scale for Children-Revised. To describe metabolic changes revealed by blood analysis. To measure treatment impact at home and in an academic environment. METHODS/DESIGN A phase II randomized, double-blind pilot clinical trial. SCOPE male children and adolescents diagnosed with FXS, in accordance with a standardized molecular biology test, who met all the inclusion criteria and none of the exclusion criteria. INSTRUMENTATION clinical data, blood analysis, Wechsler Intelligence Scale for Children-Revised, Conners parent and teacher rating scale scores and the DBC-P24 results will be obtained at the baseline (t0). Follow up examinations will take place at 12 weeks (t1) and 24 weeks (t2) of treatment. DISCUSSION A limited number of clinical trials have been carried out on children with FXS, but more are necessary as current treatment possibilities are insufficient and often provoke side effects. In the present study, we sought to overcome possible methodological problems by conducting a phase II pilot study in order to calculate the relevant statistical parameters and determine the safety of the proposed treatment. The results will provide evidence to improve hyperactivity control and reduce behavioural and learning problems using ascorbic acid (vitamin C) and α-tocopherol (vitamin E). The study protocol was approved by the Regional Government Committee for Clinical Trials in Andalusia and the Spanish agency for drugs and health products. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01329770 (29 March 2011).

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Improvement of cardiovascular risk prediction: time to review current knowledge, debates, and fundamentals on how to assess test characteristics.

Cardiovascular risk assessment might be improved with the addition of emerging, new tests derived from atherosclerosis imaging, laboratory tests or functional tests. This article reviews relative risk, odds ratios, receiver-operating curves, posttest risk calculations based on likelihood ratios, the net reclassification improvement and integrated discrimination. This serves to determine whether a new test has an added clinical value on top of conventional risk testing and how this can be verified statistically. Two clinically meaningful examples serve to illustrate novel approaches. This work serves as a review and basic work for the development of new guidelines on cardiovascular risk prediction, taking into account emerging tests, to be proposed by members of the 'Taskforce on Vascular Risk Prediction' under the auspices of the Working Group 'Swiss Atherosclerosis' of the Swiss Society of Cardiology in the future.

Propuesta de un test para determinar el conocimiento conceptual de estudiantes universitarios sobre la constante de equilibrio químico y su aplicación en estudiantes españoles

This article outlines a procedure that was used to develop a written test for evaluating the conceptual knowledge of chemical equilibrium constant among university students. The concepts in the subject matter were carefully defined through propositional statements. Students' understanding of the topic was determined through interviews. These data were used to produce nine multiple choice questions. Each question was designed to identify misconceptions related to the chemical equilibrium constant. The test was evaluated by foure associate professors and was administred to a total of 196 spanish university students. This test has a Cronbach's alpha reliability of 0.63 and its content validity values ranged from 3.7 to 5.

Automated UI testing. WBCT, a computer-automated user test environment tool.

Today, the user experience and usability in software application are becoming a major design issue due to the adaptation of many processes using new technologies. Therefore, the study of the user experience and usability might be included in every software development project and, thus, they should be tested to get traceable results. As a result of different testing methods to evaluate the concepts, a non-expert on the topic might have doubts on which option he/she should opt for and how to interpret the outcomes of the process. This work aims to create a process to ease the whole testing methodology based on the process created by Seffah et al. and a supporting software tool to follow the procedure of these testing methods for the user experience and usability.

Uso del test de cuantificación sensitiva para el estudio de la neuropatía de fibra pequeña

Introducción La mutación genética Val30Met de la proteína transtiretina (TTR) es causante de la polineuropatía amiloidótica familiar, comprometiendo en fases iniciales las fibras nerviosas pequeñas (mielinizadas Aδ y amielínicas tipo C), involucradas en funciones autonómicas, nocicepción, percepción térmica y sudoración. Los métodos neurofisiológicos convencionales, no logran detectar dichas anormalidades, retardando el inicio de tratamientos específicos para la enfermedad. Metodología El objetivo principal fue evaluar el test de cuantificación sensitiva (QST) como método de detección temprana de anormalidades de fibra pequeña, en individuos Val30Met, seguidos en el Hospital Universitario Santa María, Lisboa. Se clasificaron los pacientes en 3 grupos, según sintomatología y examen neurológico. Se analizaron los umbrales para percepción de frío, dolor con el calor y vibración en los grupos, en correlación con controles sanos. Resultados 18 registros de controles sanos y 33 de individuos con la mutación, divididos en asintomáticos (24,2%), sintomáticos con examen neurológico normal (42,4%) y sintomáticos con examen neurológico anormal (33,3%). No se encontraron diferencias entre los pacientes asintomáticos y los controles. Los umbrales para frío (p=0,042) y en el dolor intermedio con el calor (HP 5) (p=0,007) se encuentran elevados en individuos Val30Met sintomáticos con examen normal. En los pacientes sintomáticos con alteraciones al examen, también se presentaron alteraciones en el intervalo entre el inicio y el dolor intermedio con el calor (HP 5-0,5) (p=0,009). Discusión Los umbrales de frío y de percepción de dolor con el calor, permiten detectar anormalidades en personas con la mutación TTR Val30Met, sintomáticos, incluyendo aquellos sin cambios objetivos al examen neurológico.

Analyzing the CEMS intrapreneurial level using the GET2 test

This thesis is mainly focused on the intrapreneurial level of CEMS students around the world. Organizations are seeking to hire people with an entrepreneurial personality. This means that companies are looking for someone with similar psychological traits as entrepreneurs who are capable of influence positively in firm’s innovation. Therefore, the research question is “What is the CEMS intrapreneurial level?” The contribution of this thesis is to check whether CEMS students have high intrapreneurship level or not. In order to find out, the General measure Enterprising Tendency (GET) test was sent to these students. The main finding is that CEMS students have score which can located them in the Medium range, meaning they have an average level of intrapreneurship. Thus, it is possible to raise awareness on the intrapreneurial potential that they can achieve. Furthermore, this thesis hopes to grow a concern on this topic so that universities give more emphasis on it.

Cointegration: Bayesian Significance Test

To estimate causal relationships, time series econometricians must be aware of spurious correlation, a problem first mentioned by Yule (1926). To deal with this problem, one can work either with differenced series or multivariate models: VAR (VEC or VECM) models. These models usually include at least one cointegration relation. Although the Bayesian literature on VAR/VEC is quite advanced, Bauwens et al. (1999) highlighted that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results". The present article applies the Full Bayesian Significance Test (FBST), especially designed to deal with sharp hypotheses, to cointegration rank selection tests in VECM time series models. It shows the FBST implementation using both simulated and available (in the literature) data sets. As illustration, standard non informative priors are used.

Crashworthiness and composite materials: development of an experimental test method for the energy absorption determination and implementation of the relative numerical model

In this PhD thesis the crashworthiness topic is studied with the perspective of the development of a small-scale experimental test able to characterize a material in terms of energy absorption. The material properties obtained are then used to validate a nu- merical model of the experimental test itself. Consequently, the numerical model, calibrated on the specific ma- terial, can be extended to more complex structures and used to simulate their energy absorption behavior. The experimental activity started at University of Washington in Seattle, WA (USA) and continued at Second Faculty of Engi- neering, University of Bologna, Forl`ı (Italy), where the numerical model for the simulation of the experimental test was implemented and optimized.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Proving My Competency One Test at a Time: Internationally Educated Nurses and the Canadian English Language Benchmark Assessment for Nurses

Abstract Professional language assessment is a new concept that has great potential to benefit Internationally Educated Professionals and the communities they serve. This thesis reports on a qualitative study that examined the responses of 16 Canadian English Language Benchmark Assessment for Nurses (CELBAN) test-takers on the topic of their perceptions of the CELBAN test-taking experience in Ontario in the winter of 2015. An Ontario organization involved in registering participants distributed an e-mail through their listserv. Thematic analyses of focus group and interview transcripts identified 7 themes from the data. These themes were used to inform conclusions to the following questions: (1) How do IENs characterize their assessment experience? (2) How do IENs describe the testing constructs measured by the CELBAN? (3) What, if any, potential sources of construct irrelevant variance (CIV) do the test-takers describe based on their assessment experience? (4) Do IENs feel that the CELBAN tasks provide a good reflection of the types of communicative tasks required of a nurse? Overall, participants reported positive experiences with the CELBAN as an assessment of their language skills, and noted some instances in which they felt some factors external to the assessment impacted their demonstration of their knowledge and skill. Lastly, some test-takers noted the challenge of completing the CELBAN where the types of communicative nursing tasks included in the assessment differed from nursing tasks typical of an IENs country or origin. The findings are discussed in relation to literature on high-stakes large-scale assessment and IEPs, and a set of recommendations are offered to future CELBAN administration. These recommendations include (1) the provision of a webpage listing all licensure requirements (2) monitoring of CELBAN location and dates in relation to the wider certification timeline for applicants (3) The provision of additional CELBAN preparatory materials (4) Minor changes to the CELBAN administrative protocols. Given that the CELBAN is a relatively new assessment format and its widespread use for high-stakes decisions (a component of nursing certification and licensure), research validating IEN-test-taker responses to construct representation and construct irrelevant variance is critical to our understanding of the role of competency testing for IENs.

Relations among Topic Knowledge, Individual Interest, and Relational Reasoning, and Critical Thinking in Maternity Nursing

Critical thinking in learners is a goal of educators and professional organizations in nursing as well as other professions. However, few studies in nursing have examined the role of the important individual difference factors topic knowledge, individual interest, and general relational reasoning strategies in predicting critical thinking. In addition, most previous studies have used domain-general, standardized measures, with inconsistent results. Moreover, few studies have investigated critical thinking across multiple levels of experience. The major purpose of this study was to examine the degree to which topic knowledge, individual interest, and relational reasoning predict critical thinking in maternity nurses. For this study, 182 maternity nurses were recruited from national nursing listservs explicitly chosen to capture multiple levels of experience from prelicensure to very experienced nurses. The three independent measures included a domain-specific Topic Knowledge Assessment (TKA), consisting of 24 short-answer questions, a Professed and Engaged Interest Measure (PEIM), with 20 questions indicating level of interest and engagement in maternity nursing topics and activities, and the Test of Relational Reasoning (TORR), a graphical selected response measure with 32 items organized in scales corresponding to four forms of relational reasoning: analogy, anomaly, antithesis, and antinomy. The dependent measure was the Critical Thinking Task in Maternity Nursing (CT2MN), composed of a clinical case study providing cues with follow-up questions relating to nursing care. These questions align with the cognitive processes identified in a commonly-used definition of critical thinking in nursing. Reliable coding schemes for the measures were developed for this study. Key findings included a significant correlation between topic knowledge and individual interest. Further, the three individual difference factors explained a significant proportion of the variance in critical thinking with a large effect size. While topic knowledge was the strongest predictor of critical thinking performance, individual interest had a moderate significant effect, and relational reasoning had a small but significant effect. The findings suggest that these individual difference factors should be included in future studies of critical thinking in nursing. Implications for nursing education, research, and practice are discussed.