877 resultados para Attitudes, Persuasion, Confidence, Voice, Elaboration Likelihood Model
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IntroductionAn awareness of visceral leishmaniasis (VL) is necessary to encourage the population to participate in prevention and control in collaboration with more efficient, centrally organized health programs. The aim of this study was to evaluate the awareness of the riverside population regarding VL and the association between awareness and the prevalence of canine visceral leishmaniasis (CVL).MethodsIn total, 71 people living in riverside areas in the City of Mossoró in State of Rio Grande do Norte participated of the study, and 71 dogs were tested for CVL by polymerase chain reaction (PCR). Association analysis of several variables related to knowledge of the riverside population regarding CVL positivity was performed, yielding odds ratios (OR) and 95% confidence intervals (CI), and significance was determined using chi-square (χ2) and Fisher's exact tests.ResultsAmong individuals whose dogs tested positive for CVL, 60% did not know the cure for CVL, and these subjects were three times more likely to have a dog test positive for CVL than those who were aware the cure for CVL. Knowledge of CVL cure was the only variable that remained in the logistic model after the successive removal of variables, with an adjusted OR of 3.11 (95%CI: 1.1-8,799; p=0.032).ConclusionsInsufficient awareness regarding VL in riverside areas with CVL-positive dogs was associated with increased rates of canine infection, which suggests that changes in habits and the adoption of attitudes and preventive practices may contribute to the control and prevention of this disease. This study reinforces the need to invest in better health education programs regarding VL.
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This Letter presents a search at the LHC for s-channel single top-quark production in proton-proton collisions at a centre-of-mass energy of 8 TeV. The analyzed data set was recorded by the ATLAS detector and corresponds to an integrated luminosity of 20.3 fb−1. Selected events contain one charged lepton, large missing transverse momentum and exactly two b-tagged jets. A multivariate event classifier based on boosted decision trees is developed to discriminate s-channel single top-quark events from the main background contributions. The signal extraction is based on a binned maximum-likelihood fit of the output classifier distribution. The analysis leads to an upper limit on the s-channel single top-quark production cross-section of 14.6 pb at the 95% confidence level. The fit gives a cross-section of σs=5.0±4.3 pb, consistent with the Standard Model expectation.
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A search for the Standard Model Higgs boson produced in association with a pair of top quarks, tt¯H, is presented. The analysis uses 20.3 fb−1 of pp collision data at s√ = 8 TeV, collected with the ATLAS detector at the Large Hadron Collider during 2012. The search is designed for the H to bb¯ decay mode and uses events containing one or two electrons or muons. In order to improve the sensitivity of the search, events are categorised according to their jet and b-tagged jet multiplicities. A neural network is used to discriminate between signal and background events, the latter being dominated by tt¯+jets production. In the single-lepton channel, variables calculated using a matrix element method are included as inputs to the neural network to improve discrimination of the irreducible tt¯+bb¯ background. No significant excess of events above the background expectation is found and an observed (expected) limit of 3.4 (2.2) times the Standard Model cross section is obtained at 95% confidence level. The ratio of the measured tt¯H signal cross section to the Standard Model expectation is found to be μ=1.5±1.1 assuming a Higgs boson mass of 125 GeV.
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The Paternal Adjustment and Paternal Attitudes Questionnaire (PAPA) was designed to assess paternal adjustment and paternal attitudes during the transition to parenthood. This study aimed to examine the psychometric characteristics of the Portuguese versions of the PAPA-Antenatal (PAPA-AN) and -Postnatal (PAPA-PN) versions. A nonclinical sample of 128 fathers was recruited in the obstetrics outpatient unit, and they completed both versions of the PAPA and selfreport measures of depressive and anxiety symptoms during pregnancy and the postpartum period, respectively. Good internal consistency for both PAPA-AN and PAPA-PN was found. A three-factor model was found for both versions of the instrument. Longitudinal confirmatory factor analysis revealed a good model fit. The PAPA-AN and PAPA-PN subscales revealed good internal consistency. Significant associations were found between PAPA (PAPA-AN and PAPA-PN) and depressive and anxiety symptoms, suggesting good criterion validity. Both versions also showed good clinical validity, with optimal cutoffs found. The present study suggested that the Portuguese versions of the PAPA are reliable multidimensional self-report measures of paternal adjustment and paternal attitudes that could be used to identify fathers with adjustment problems and negative attitudes during the transition to parenthood.
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Ever since the appearance of the ARCH model [Engle(1982a)], an impressive array of variance specifications belonging to the same class of models has emerged [i.e. Bollerslev's (1986) GARCH; Nelson's (1990) EGARCH]. This recent domain has achieved very successful developments. Nevertheless, several empirical studies seem to show that the performance of such models is not always appropriate [Boulier(1992)]. In this paper we propose a new specification: the Quadratic Moving Average Conditional heteroskedasticity model. Its statistical properties, such as the kurtosis and the symmetry, as well as two estimators (Method of Moments and Maximum Likelihood) are studied. Two statistical tests are presented, the first one tests for homoskedasticity and the second one, discriminates between ARCH and QMACH specification. A Monte Carlo study is presented in order to illustrate some of the theoretical results. An empirical study is undertaken for the DM-US exchange rate.
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We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson, hurdle semiparametric Poisson, and finite mixtures of negative binomial models to six measures of health care usage taken from the Medical Expenditure Panel survey. We conclude that most of the models lead to statistically similar results, both in terms of information criteria and conditional and unconditional prediction. This suggests that applied researchers may not need to be overly concerned with the choice of which of these models they use to analyze data on health care demand.
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This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model’s posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model’s errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model’s fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.
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We propose a non-equidistant Q rate matrix formula and an adaptive numerical algorithm for a continuous time Markov chain to approximate jump-diffusions with affine or non-affine functional specifications. Our approach also accommodates state-dependent jump intensity and jump distribution, a flexibility that is very hard to achieve with other numerical methods. The Kolmogorov-Smirnov test shows that the proposed Markov chain transition density converges to the one given by the likelihood expansion formula as in Ait-Sahalia (2008). We provide numerical examples for European stock option pricing in Black and Scholes (1973), Merton (1976) and Kou (2002).
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This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregressive process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The Öltered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidenced for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.
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This paper introduces a State Space approach to explain the dynamics of rent growth, expected returns and Price-Rent ratio in housing markets. According to the present value model, movements in price to rent ratio should be matched by movements in expected returns and expected rent growth. The state space framework assume that both variables follow an autoregression process of order one. The model is applied to the US and UK housing market, which yields series of the latent variables given the behaviour of the Price-Rent ratio. Resampling techniques and bootstrapped likelihood ratios show that expected returns tend to be highly persistent compared to rent growth. The filtered expected returns is considered in a simple predictability of excess returns model with high statistical predictability evidence for the UK. Overall, it is found that the present value model tends to have strong statistical predictability in the UK housing markets.
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Practice guidelines recommend outpatient care for selected patients with non-massive pulmonary embolism (PE), but fail to specify how these low-risk patients should be identified. Using data from U.S. patients, we previously derived the Pulmonary Embolism Severity Index (PESI), a prediction rule that risk stratifies patients with PE. We sought to validate the PESI in a European patient cohort. We prospectively validated the PESI in patients with PE diagnosed at six emergency departments in three European countries. We used baseline data for the rule's 11 prognostic variables to stratify patients into five risk classes (I-V) of increasing probability of mortality. The outcome was overall mortality at 90 days after presentation. To assess the accuracy of the PESI to predict mortality, we estimated the sensitivity, specificity, and predictive values for low- (risk classes I/II) versus higher-risk patients (risk classes III-V), and the discriminatory power using the area under the receiver operating characteristic (ROC) curve. Among 357 patients with PE, overall mortality was 5.9%, ranging from 0% in class I to 17.9% in class V. The 186 (52%) low-risk patients had an overall mortality of 1.1% (95% confidence interval [CI]: 0.1-3.8%) compared to 11.1% (95% CI: 6.8-16.8%) in the 171 (48%) higher-risk patients. The PESI had a high sensitivity (91%, 95% CI: 71-97%) and a negative predictive value (99%, 95% CI: 96-100%) for predicting mortality. The area under the ROC curve was 0.78 (95% CI: 0.70-0.86). The PESI reliably identifies patients with PE who are at low risk of death and who are potential candidates for outpatient care. The PESI may help physicians make more rational decisions about hospitalization for patients with PE.
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(Résumé de l'ouvrage) This volume addresses research topics within the field of Bhakti literature, the devotional poetry and other compositions of devotional character in the earlier literature of the modern South Asian languages. Its papers range from the roots of the Bhakti tradition in the early history of Krsna to its modern adaptations in nineteenth- and twentieth-century culture. Geographically, they span Bengal to Sind, Panjab to Maharashtra. Contemporary study of the modern Indian languages has broadened the scope of scholarship to consider today's Hindu attitudes, and those of a mixed society, against the background of ancient culture. Here, materials in six modern Asian languages are discussed: Bengali, Gujarati, Hindi in its main literary forms, Marathi, Panjabi and Sindhi; with assessment also of material in Sanskrit, Arabic and Chinese. In addition to studies of literary (and orally transmitted) works in the Krsna or Rama traditions, and of Sufi compositions and their interpretation, there are papers on the early history of sacred sites, the emergence of the religion of Rama, later religious formulations throughout the subcontinent, and the interaction of the Islamic and the Hindu.
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Background Individual signs and symptoms are of limited value for the diagnosis of influenza. Objective To develop a decision tree for the diagnosis of influenza based on a classification and regression tree (CART) analysis. Methods Data from two previous similar cohort studies were assembled into a single dataset. The data were randomly divided into a development set (70%) and a validation set (30%). We used CART analysis to develop three models that maximize the number of patients who do not require diagnostic testing prior to treatment decisions. The validation set was used to evaluate overfitting of the model to the training set. Results Model 1 has seven terminal nodes based on temperature, the onset of symptoms and the presence of chills, cough and myalgia. Model 2 was a simpler tree with only two splits based on temperature and the presence of chills. Model 3 was developed with temperature as a dichotomous variable (≥38°C) and had only two splits based on the presence of fever and myalgia. The area under the receiver operating characteristic curves (AUROCC) for the development and validation sets, respectively, were 0.82 and 0.80 for Model 1, 0.75 and 0.76 for Model 2 and 0.76 and 0.77 for Model 3. Model 2 classified 67% of patients in the validation group into a high- or low-risk group compared with only 38% for Model 1 and 54% for Model 3. Conclusions A simple decision tree (Model 2) classified two-thirds of patients as low or high risk and had an AUROCC of 0.76. After further validation in an independent population, this CART model could support clinical decision making regarding influenza, with low-risk patients requiring no further evaluation for influenza and high-risk patients being candidates for empiric symptomatic or drug therapy.
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It has been argued that by truncating the sample space of the negative binomial and of the inverse Gaussian-Poisson mixture models at zero, one is allowed to extend the parameter space of the model. Here that is proved to be the case for the more general three parameter Tweedie-Poisson mixture model. It is also proved that the distributions in the extended part of the parameter space are not the zero truncation of mixed poisson distributions and that, other than for the negative binomial, they are not mixtures of zero truncated Poisson distributions either. By extending the parameter space one can improve the fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. Considering the extended model also allows one to use the basic maximum likelihood based inference tools when parameter estimates fall in the extended part of the parameter space, and hence when the m.l.e. does not exist under the unextended model. This extended truncated Tweedie-Poisson model is proved to be useful in the analysis of words and species frequency count data.
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L'adaptació dels estudis a l'Espai Europeu d'Educació Superior (EEES) comporta l'establiment d'un nou paradigma docent centrat en l'estudiant, l'ús d'una nova unitat de valoració de l'activitat acadèmica, el crèdit europeu, i enfortir i repensar l'avaluació de l'aprenentatge. Aquesta adaptació representa una transformació profunda en el paper que juga tant el professor com l'alumne. En aquest context, es fa molt necessari adaptar les metodologies docents, els continguts i les eines d'interacció amb els alumnes. La finalitat d'aquest projecte consisteix en adaptar les assignatures troncals d'Organització i Mètodes de Treball de la Diplomatura de Relacions Laborals a aquest nou entorn. Per a tal fi, a part d'elaborar un pla docent consistent al EEES, també s'han de desenvolupar continguts, eines d'interacció i metodologies d'avaluació que permetin millorar el nivell de formació i el rendiment acadèmic de l'estudiant. Aquest projecte comprèn sis accions específiques per aconseguir-ho: 1) L'elaboració de la guia docent (objectius, capacitats a treballar, continguts, metodologia, avaluació, etc.); 2) L'elaboració del programa d'activitats (on es concreten les activitats d'aprenentatge que l'estudiant ha de dur a terme per assolir els objectius formatius: projectes tutoritzats, estudis de casos , lectures orientades, etc.) 3) Elaboració del material teòric. Aquesta acció té com a objectiu realitzar un llibre bàsic que comprengui la totalitat de la matèria relacionada amb OMT. 4) Elaboració del material pràctic. 5) Creació d'una pàgina web per aquest material. Aquesta web pretén ser un espai de treball comú pels estudiants de Relacions Laborals que cursin assignatures d'OMT a la UAB i la UPF. 6) Realitzar un projecte pilot tutoritzat. Aquesta prova pilot sorgeix arran de converses informals mantingudes amb alumnes de la diplomatura i la coordinadora d'estudis.