Suchergebnisse

Die westlichen Demokratien sehen sich zunehmend durch autoritär-populistische Strömungen herausgefordert. Die Annahme, dass demokratische Lernerfahrungen unumkehrbar sind, ist fragwürdig geworden. Lässt sich ein stabiler Trend zur "demokratischen Regression" (Schäfer/Zürn) erkennen? Was sind die Anhaltspunkte, auf die sich eine solche Diagnose stützen lässt, und was sind mögliche Ursachen? Was fügt der Begriff der Regression der allgegenwärtigen Rede vom Niedergang der Demokratie hinzu? Der Sonderband leistet die erste umfassende Erörterung der empirischen Diagnosen, analytischen Bestimmungen und normativen Verwendungen von Phänomen und Begriff demokratischer Regression. Mit Beiträgen von Svenja Ahlhaus | Rainer Forst | Jakob Huber | Jasmin Sarah König | Claudia Landwehr | Philip Manow | Peter Niesen | Norma Osterberg-Kaufmann | Markus Patberg | Armin Schäfer | Tilko Swalve | Stefan Voigt | Jonathan White | Fabio Wolkenstein | Michael Zürn.

AbstractRessentiment seems to be one of the key concepts of our time. But what is the use of the concept of ressentiment for understanding and analyzing the rise of antigender, antimigrant, antiegalitarian, antidemocratic, homo- and transphobic, and masculinist as well as anti-Semitic and anti-Muslim sentiments, as they are articulated in populist movements all over the globe in varying constellations and to different degrees? This essay argues that, although it is a productive category for the diagnosis of our times, ressentiment alone is too weak a tool for critical theory. In order not to lose its force and not to become a psychologizing and individualizing interpretative term, ressentiment needs to be understood as a mode of regression and therefore should be embedded in a theoretical framework for understanding crisis that allows us to address the social structures that enable, necessitate, and nourish ressentiments.

To investigate the predictors of employment status of patients with DSM-III-R diagnosis, 55 patients were selected by a simple random technique from the main psychiatric clinic in Al Ain, United Arab Emirates. Structured and formal assess ments were carried out to extract the potential predictors of outcome of schizo phrenia. Logistic regression model revealed that being married, absence of schizoid personality, free or with minimum symptoms of the illness, later age of onset, and higher educational attainment were the most significant predictors of employment outcome. The implications of the results of this study are discussed in the text.

Zur Diagnose demokratischer RegressionEd. by Peter Niesen[Leviathan Sonderband 40](Nomos, August 2023)338 pagesDescriptionWestern democracies are increasingly being challenged by authoritarian populism. The assumption that democratic learning experiences are irreversible has become questionable. Can we identify a stable trend towards "democratic regression" (Schäfer/Zürn)? What are the criteria on which such a diagnosis can be based, and what are the possible causes of such developments? What does the concept of regression add to the ubiquitous talk of the decline of democracy? This special issue provides the first comprehensive discussion of the empirical diagnoses, analytical determinations and normative uses of the phenomenon and concept of democratic regressionContents* Einleitung1. Zur Diagnose demokratischer Regression. Annahmen, Merkmale, Herausforderungen [adjusted excerpt] - Peter Niesen* Symptome und Merkmale demokratischer Regression2. Republikanismus, Repräsentation und Regression - Armin Schäfer3. Die regulative Idee der Wahrheit und demokratische Regression - Michael Zürn4. Eine Beobachtung der Demokratiebeobachtung. Zur Diagnose demokratischer Regression - Philip Manow5. Demokratie im Zeichen des Notstands - Jonathan White* Politische Theorie der Regression6. Eine demokratische Theorie demokratischer Regressionen [paper] - Fabio Wolkenstein7. Regression und Erneuerung der Demokratie: eine psychoanalytische Perspektive - Claudia Landwehr8. Exit-Politik als Regression. Wider den souveränen Voluntarismus [paper] - Svenja Ahlhaus & Markus Patberg* Nicht-Regression und Fortschritt9. Die Herrschaft der Unvernunft. Zum Begriff der (anti-)demokratischen Regression [paper] - Rainer Forst10. Der Imperativ der Nicht-Regression. Adorno, Habermas und die Pfadabhängigkeit von Sperrklinkeneffekten - Peter Niesen11. Kritik der Regression - Jakob Huber* Konstitutionelle Demokratie und die Pluralisierung des Demokratieverständnisses12. Zum Verhältnis von demokratischer und konstitutioneller Regression unter populistischen Regierungen. Eine empirische Analyse [paper] - Jasmin Sarah König & Tilko Swalve13. Nichtmajoritäre Institutionen – eine Gefahr für die konstitutionelle Demokratie? - Stefan Voigt14. Das Demokratieverständnis der Bevölkerung und die Regression der Demokratie - Norma Osterberg-Kaufmann

Controlling the spread of the Covid-19 virus in Indonesia, the government continues to strive for a comprehensive 3T (Testing, Training, and Tracing) implementation. Massive testing is often constrained by several things, including cost and affordability of access. This study aims to create a model for early diagnosis of Covid-19 infection cases through several characteristic symptoms and experiences of close contact with positive patients. By using a binary logistic regression model, it was found that symptoms of anosmia, feverish symptoms, and close contact experience were significant in influencing Covid-19 infection. From the odds ratio value, it is known that anosmia is the most influential variable. Someone who has anosmia tends to be infected by 31 times compared to those who do not. Validation of the strength of the model in classifying is done by making predictions on the resulting model is good, because the measurement of each criterion of the strength of the model consists of accuracy, sensitivity, and specificity of the model both on the data testing each produces a value of 0.8 (close to 1). The area under the Receiver Operating Characteristic (ROC) curve for testing data is 0.8462, which means that the model already has good criteria for classifying.

Diagnosing foot complaints using plantar pressure videos is complicated by the presence of confounding factors (e.g. age, weight). Outlier detection could help with diagnosis, but these confounding factors result in data that is not independent and identically distributed (IID) with respect to a specific patient. To address this non-IID problem, we propose the modeling of confounding factors using metric learning. A distance metric is learned on the confounding factors in order to model their impact on the plantar pressures. This metric is then employed to weight plantar pressures from healthy controls when generating a patient-specific statistical baseline. Statistical parametric mapping is then used to compare the patient to this statistical baseline. We show that using metric learning reduces variance in these statistical baselines, which then improves the sensitivity of the outlier detection. These improvements in outlier detection get us one step closer to accurate computer-aided diagnosis of foot complaints. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 746614.

Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques.

This study forms part of the Doctoral Thesis of the first-named author (Maria Jose Membrive-Jimenez). Funding for this study was provided by the Andalusian Government Excellence Project (P11-HUM-7771). ; Nurse managers are affected by burnout due to the high degree of interaction between managers with their registered nurses. Explanatory models based on psychological, and personality related variables purvey an estimation to level changes in the three dimensions of the burnout syndrome. A categorical-response logistic ordinal regression model, supported on a quantitative, crosscutting, multicentre, descriptive study with 86 nursing managers in the Andalusian Health Service in Granada, Spain is performed for each dimension. The three models included different variables related to personality, as well as depression as the only explanatory variable included in all the models. The risk factor neuroticism was significant at population level and related to emotional exhaustion, whilst responsibility was significant in the model estimated to personal accomplishment dimension. Finally, depression was significant for the three dimensions of Burnout. This analysis provides useful information to help the diagnosis and evolution of this syndrome in this collective. ; Andalusian Government Excellence Project P11-HUM-7771

AbstractIn recent years, an increasing number of health diagnosis mobile apps have been developed and marketed to assist health professionals in the process of diagnosis. Yet, there is limited knowledge about the factors and app characteristics that affect their selection from health professionals. In this study, we investigate the specific apps' market that is addressed to medical professionals/students in order to explain how the specific consumers' behavior is affected by certain app characteristics and attributes. We based our model on the combination of two theoretical models, the Diffusion of Innovation (DOI) and the Technology Acceptance Model (TAM) to investigate the criteria for the intention of adoption of mobile apps in clinical routine. An evaluation framework (MARS) has been used to measure the quality of each app and text processing has been applied to retrieve and code additional informative variables from the descriptions and users' reviews. To investigate the relationships between app quality, downloads and users' ratings we used multiple linear regression statistical analysis. The results showed that the number of apps downloads is positively related to users' usefulness, star rating, and app quality while downloads are also correlated to the number of reviews, long app description, years since first release, and in‐app ads. This study contributes to the information systems and mobile health literature in providing a better understanding of which quality characteristics of mobile apps have an impact on their popularity and evaluation and how their functionalities and quality affect the professionals' decision process.

Many new statistical models may enjoy better interpretability and numerical stability than traditional models in survival data analysis. Specifically, the threshold regression (TR) technique based on the inverse Gaussian distribution is a useful alternative to the Cox proportional hazards model to analyse lifetime data. In this article we consider a semi‐parametric modelling approach for TR and contribute implementational and theoretical details for model fitting and statistical inferences. Extensive simulations are carried out to examine the finite sample performance of the parametric and non‐parametric estimates. A real example is analysed to illustrate our methods, along with a careful diagnosis of model assumptions.

Recent findings indicate that the number of newborns with Sickle cell anemia (SCA) will increase on the horizon 2050. Among the concerned countries, Democratic Republic of the Congo will probably still be the country most in need of policies for the prevention and management of SCA. The present fundamental research was carried out with the aim of providing data to help future design and the development of new generation of techniques for the low cost diagnosis of SCA patients from low income countries. The interest in the protein thermal denaturation is due to its potential to easily quantify any change in the hemoglobin stability. The thermal denaturation of hemoglobin in human total blood samples was studied using molecular ultraviolet/visible absorption method. The result expressed as transition temperature (Tt) displayed the best discrimination between AA, AS and SS bloods at pH 7.40 and without ionic strength. Calculated values of Tt according to a non-linear regression analysis using the Gushimana Yav equation (a sigmoid plot) with the help of Microsoft Origin package are 65.2 ± 0.1 °C for AA blood, 61.0 ± 0.1 °C for AS blood and 58.0 ± 0.1 °C for SS blood. By considering Tt as a specific physical parameter, it is thus proposed to design and develop new generation of spectrophotometer for the biological diagnosis of SCA.