Síndrome de Burnout e autoeficácia: um estudo com profissionais de enfermagem de hospitais privados de Natal/RN

This research verifies the influence of the self-efficacy level on burnout syndrome incidence in relation to nursing professionals from private hospitals located in the Municipality of Natal, State of Rio Grande do Norte. The nature of the research was descriptive, and the used data analysis method was quantitative which was developed through SPSS computational package, version 17.0. The used instrument for the investigation was Maslach-Burnout Inventory (MBI), and the General Perceived Self-efficacy Scale (GPSES) was applied to a sample formed from 230 nursing professionals. The statistic techniques to data analysis were: frequency analysis; factor analysis; Cronbach.s alpha; Kaiser-Meyer-Olkin test (KMO); Bartlett efericity test; percentual analysis; Spearman rank correlation analysis; and simple regression. The achieved factors from factor analysis of MBI were the same, taking into account the dimensions which Maslach initially suggested to the instrument (emotional exhaustion, lack of personal realization, and depersonalization). However, one highlights that the low internal consistence of the depersonalization dimension can occur from people.s difficulty (caused by cultural aspects) of assuming this attitude in their work environment. Through GSE, it was achieved a factor which confirmed the unidimensionality showed by the author of the instrument. In relation to the syndrome incidence, it was verified that about 50% of the researched sample presented burnout syndrome evidence. Referring to self-efficacy level, about 65% of the researched sample presented low level of self-efficacy, what can be explained by the work characteristics of these professionals. In relation to the self-efficacy influence on the Burnout syndrome, it was verified that self-efficacy can be one of the aspects which influences occupational stress chronification (burnout), mainly to the personal realization dimension. Therefore, the researched hospital organizations need reflect about their attitudes in respect to their professionals, since the numbers showed a dangerous tendency regarding a predisposition to burnout syndrome of their staff, what implies not only a significant amount of individuals who can present high levels of emotional exhaustion, lack of personal realization, and depersonalization, but also the fact that this group presents low level of self-efficacy

Two years survival rate of Class II ART restorations in primary molars using two ways to avoid saliva contamination

AIM To compare the survival rates of Class II Atraumatic Restorative Treatment (ART) restorations placed in primary molars using cotton rolls or rubber dam as isolation methods. METHODS A total of 232 children, 6-7 years old, both genders, were selected having one primary molar with proximal dentine lesion. The children were randomly assigned into two groups: control group with Class II ART restoration made using cotton rolls and experimental group using rubber dam. The restorations were evaluated by eight calibrated evaluators (Kappa > 0.8) after 6, 12, 18 and 24 months. RESULTS A total of 48 (20.7%) children were considered dropout, after 24 months. The cumulative survival rate after 6, 12, 18 and 24 months was 61.4%, 39.0%, 29.1% and 18.0%, respectively for the control group, and 64.1%, 55.1%, 40.1% and 32.1%, respectively for the rubber dam group. The log rank test for censored data showed no statistical significant difference between the groups (P = 0.07). The univariate Cox Regression showed no statistical significant difference after adjusting for independent variables (P > 0.05). CONCLUSION Both groups had similar survival rates, and after 2 years, the use of rubber dam does not increase the success of Class II ART restorations significantly.

Varianzanalysen - Prüfen der Voraussetzungen und nichtparametrische Methoden sowie praktische Anwendungen mit R und SPSS. Version 1.1 (2.3.2015)

Die Voraussetzunen der parametrischen 1- und mehrfaktoriellen Varianzanalyse, mit und ohne Messwiederholungen, werden besprochen. Ferner werden eine Reihe von alternativen Verfahren vorgestellt, insbesondere einige nichtparametrische, darunter RT (rank transform), INT (inverse normal transform), ART (aligned rank transform), Puri & Sen (L statistic), van der Waerden und Akritas & Brunner (ATS anova type statistic), die sich auf die parametrische Varianzanalyse zurückführen lassen, sowie dichotome und ordinale logistische Regression. Hierzu werden Lösungen mit R und SPSS ausführlich gezeigt.

Loan quality, ownership and efficiency of Indian banks : A bootstrap truncated regression approach

Registration-by-regression of coronary CTA and X-ray angiography

We evaluate the integration of 3D preoperative computed tomography angiography of the coronary arteries with intraoperative 2D X-ray angiographies by a recently proposed novel registration-by-regression method. The method relates image features of 2D projection images to the transformation parameters of the 3D image. We compared different sets of features and studied the influence of preprocessing the training set. For the registration evaluation, a gold standard was developed from eight X-ray angiography sequences from six different patients. The alignment quality was measured using the 3D mean target registration error (mTRE). The registration-by-regression method achieved moderate accuracy (median mTRE of 15 mm) on real images. It does therefore not provide yet a complete solution to the 3D–2D registration problem but it could be used as an initialisation method to eliminate the need for manual initialisation.

CROSS-LINGUISTIC TRANSFER OF SPELLING SKILLS IN SPANISH-SPEAKING ADULT ESL LEARNERS

Spelling is an important literacy skill, and learning to spell is an important component of learning to write. Learners with strong spelling skills also exhibit greater reading, vocabulary, and orthographic knowledge than those with poor spelling skills (Ehri & Rosenthal, 2007; Ehri & Wilce, 1987; Rankin, Bruning, Timme, & Katkanant, 1993). English, being a deep orthography, has inconsistent sound-to-letter correspondences (Seymour, 2005; Ziegler & Goswami, 2005). This poses a great challenge for learners in gaining spelling fluency and accuracy. The purpose of the present study is to examine cross-linguistic transfer of English vowel spellings in Spanish-speaking adult ESL learners. The research participants were 129 Spanish-speaking adult ESL learners and 104 native English-speaking GED students enrolled in a community college located in the South Atlantic region of the United States. The adult ESL participants were in classes at three different levels of English proficiency: advanced, intermediate, and beginning. An experimental English spelling test was administered to both the native English-speaking and ESL participants. In addition, the adult ESL participants took the standardized spelling tests to rank their spelling skills in both English and Spanish. The data were analyzed using robust regression and Poisson regression procedures, Mann-Whitney test, and descriptive statistics. The study found that both Spanish spelling skills and English proficiency are strong predictors of English spelling skills. Spanish spelling is also a strong predictor of level of L1-influenced transfer. More proficient Spanish spellers made significantly fewer L1-influenced spelling errors than less proficient Spanish spellers. L1-influenced transfer of spelling knowledge from Spanish to English likely occurred in three vowel targets (/ɑɪ/ spelled as ae, ai, or ay, /ɑʊ/ spelled as au, and /eɪ/ spelled as e). The ESL participants and the native English-speaking participants produced highly similar error patterns of English vowel spellings when the errors did not indicate L1-influenced transfer, which implies that the two groups might follow similar trajectories of developing English spelling skills. The findings may help guide future researchers or practitioners to modify and develop instructional spelling intervention to meet the needs of adult ESL learners and help them gain English spelling competence.

Data-driven Automatic Edge Sharpening

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths. We present a method that enhances sharpness of images and thereby their perceptual quality. Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs. When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters. We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.

Flexible regression models for functional neuroimaging

Current practice for analysing functional neuroimaging data is to average the brain signals recorded at multiple sensors or channels on the scalp over time across hundreds of trials or replicates to eliminate noise and enhance the underlying signal of interest. These studies recording brain signals non-invasively using functional neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) generate complex, high dimensional and noisy data for many subjects at a number of replicates. Single replicate (or single trial) analysis of neuroimaging data have gained focus as they are advantageous to study the features of the signals at each replicate without averaging out important features in the data that the current methods employ. The research here is conducted to systematically develop flexible regression mixed models for single trial analysis of specific brain activities using examples from EEG and MEG to illustrate the models. This thesis follows three specific themes: i) artefact correction to estimate the `brain' signal which is of interest, ii) characterisation of the signals to reduce their dimensions, and iii) model fitting for single trials after accounting for variations between subjects and within subjects (between replicates). The models are developed to establish evidence of two specific neurological phenomena - entrainment of brain signals to an $\alpha$ band of frequencies (8-12Hz) and dipolar brain activation in the same $\alpha$ frequency band in an EEG experiment and a MEG study, respectively.

Recomendr-entity recommendation based on ad-hoc dimensions

The growing availability and popularity of opinion rich resources on the online web resources, such as review sites and personal blogs, has made it convenient to find out about the opinions and experiences of layman people. But, simultaneously, this huge eruption of data has made it difficult to reach to a conclusion. In this thesis, I develop a novel recommendation system, Recomendr that can help users digest all the reviews about an entity and compare candidate entities based on ad-hoc dimensions specified by keywords. It expects keyword specified ad-hoc dimensions/features as input from the user and based on those features; it compares the selected range of entities using reviews provided on the related User Generated Contents (UGC) e.g. online reviews. It then rates the textual stream of data using a scoring function and returns the decision based on an aggregate opinion to the user. Evaluation of Recomendr using a data set in the laptop domain shows that it can effectively recommend the best laptop as per user-specified dimensions such as price. Recomendr is a general system that can potentially work for any entities on which online reviews or opinionated text is available.

Spontaneous Cirrhosis Regression in an IFN-beta-induced AIH-like Syndrome Following Drug Withdrawal: Art of Facts or Artifacts?

Autoimmune hepatitis (AIH) is a disease of unknown aetiology with drug-induced AIH being the most complex and not fully understood type. We present the case of a 57-year-old female patient with acute icteric hepatitis after interferon-beta-1b (IFNβ-1b) administration for multiple sclerosis (MS). Based on liver autoimmune serology, histology and appropriate exclusion of other liver diseases, a diagnosis of AIH-related cirrhosis was established. Following discontinuation of IFNβ-1b, a complete resolution of biochemical activity indices was observed and the patient remained untreated on her own decision. However, 3 years later, after a course of intravenous methylprednisolone for MS, a new acute transaminase flare was recorded which subsided again spontaneously after 3 weeks. Liver biopsy and elastography showed significant fibrosis regression (F2 fibrosis). To our knowledge, this is the first report showing spontaneous cirrhosis regression in an IFNβ-1b-induced AIH-like syndrome following drug withdrawal, suggesting that cirrhosis might be reversible if the offending fibrogenic stimulus is withdrawn.

Rank metric convolutional codes

In this contribution, we propose a first general definition of rank-metric convolutional codes for multi-shot network coding. To this aim, we introduce a suitable concept of distance and we establish a generalized Singleton bound for this class of codes.

Explaining dinophysis cf. acuminata abundance in Antifer (Normandy, France) using dynamic linear regression

Classical regression analysis can be used to model time series. However, the assumption that model parameters are constant over time is not necessarily adapted to the data. In phytoplankton ecology, the relevance of time-varying parameter values has been shown using a dynamic linear regression model (DLRM). DLRMs, belonging to the class of Bayesian dynamic models, assume the existence of a non-observable time series of model parameters, which are estimated on-line, i.e. after each observation. The aim of this paper was to show how DLRM results could be used to explain variation of a time series of phytoplankton abundance. We applied DLRM to daily concentrations of Dinophysis cf. acuminata, determined in Antifer harbour (French coast of the English Channel), along with physical and chemical covariates (e.g. wind velocity, nutrient concentrations). A single model was built using 1989 and 1990 data, and then applied separately to each year. Equivalent static regression models were investigated for the purpose of comparison. Results showed that most of the Dinophysis cf. acuminata concentration variability was explained by the configuration of the sampling site, the wind regime and tide residual flow. Moreover, the relationships of these factors with the concentration of the microalga varied with time, a fact that could not be detected with static regression. Application of dynamic models to phytoplankton time series, especially in a monitoring context, is discussed.

Síndrome de Burnout e autoeficácia: um estudo com profissionais de enfermagem de hospitais privados de Natal/RN

This research verifies the influence of the self-efficacy level on burnout syndrome incidence in relation to nursing professionals from private hospitals located in the Municipality of Natal, State of Rio Grande do Norte. The nature of the research was descriptive, and the used data analysis method was quantitative which was developed through SPSS computational package, version 17.0. The used instrument for the investigation was Maslach-Burnout Inventory (MBI), and the General Perceived Self-efficacy Scale (GPSES) was applied to a sample formed from 230 nursing professionals. The statistic techniques to data analysis were: frequency analysis; factor analysis; Cronbach.s alpha; Kaiser-Meyer-Olkin test (KMO); Bartlett efericity test; percentual analysis; Spearman rank correlation analysis; and simple regression. The achieved factors from factor analysis of MBI were the same, taking into account the dimensions which Maslach initially suggested to the instrument (emotional exhaustion, lack of personal realization, and depersonalization). However, one highlights that the low internal consistence of the depersonalization dimension can occur from people.s difficulty (caused by cultural aspects) of assuming this attitude in their work environment. Through GSE, it was achieved a factor which confirmed the unidimensionality showed by the author of the instrument. In relation to the syndrome incidence, it was verified that about 50% of the researched sample presented burnout syndrome evidence. Referring to self-efficacy level, about 65% of the researched sample presented low level of self-efficacy, what can be explained by the work characteristics of these professionals. In relation to the self-efficacy influence on the Burnout syndrome, it was verified that self-efficacy can be one of the aspects which influences occupational stress chronification (burnout), mainly to the personal realization dimension. Therefore, the researched hospital organizations need reflect about their attitudes in respect to their professionals, since the numbers showed a dangerous tendency regarding a predisposition to burnout syndrome of their staff, what implies not only a significant amount of individuals who can present high levels of emotional exhaustion, lack of personal realization, and depersonalization, but also the fact that this group presents low level of self-efficacy