Gaya Kepemimpinan Perempuan dalam Jabatan Publik (Studi Kasus: Lurah Perempuan di Kelurahan Kesiman Kecamatan Depasar Timur)

Penelitian ini bertujuan untuk mengetahui secara aktual bagaimana gaya kepemimpinan lurah perempuan di Kelurahan Kesiman. Penelitian ini melihat gaya kepemimpinan dari dua sisi yang berbeda yaitu gaya kepemimpinan secara umum dan gaya kepemimpinan khas perempuan. gaya kepemimpinan secara umum terdiri dari gaya kepemimpinan otoriter, demokratis, dan laissez-faire. Sedangkan gaya kepemimpinan khas perempuan terdiri dari maskulin - feminim dan transformasional - transaksional. Metode yang digunakan dalam penelitian ini adalah jenis penelitian kualitatif deskritif. Adapun hasil penelitian menunjukkan bahwa gaya kepemimpinan yang digunakan oleh lurah perempuan di Kesiman adalah gaya kepemimpinan demokratis jika dilihat dari sisi gaya kepemimpinan secara umum, dan gaya kepemimpinan maskulin-transformasional jika dilihat dari gaya kepemimpinan khas perempuan

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

Epilepsy is a disease that decreases the quality of life of patients; it is also among the most common neurological diseases. Several studies have approached the classification and prediction of seizures by using electroencephalographic data and machine learning techniques. A large diversity of features has been extracted from electroencephalograms to perform classification tasks; therefore, it is important to use feature selection methods to select those that leverage pattern recognition. In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT and Siena Scalp EEG databases. The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. The best F1-score (0.90) was reported by the K-nearest neighbor along with the CHB-MIT dataset. Results showed that none of the feature selection methods clearly outperformed the rest of the methods, as the performance was notably affected by the classifier, dataset, and feature set. Two of the combinations (classifier/feature selection method) reporting the best results were K-nearest neighbor/support vector machine and random forest/embedded random forest

Genomic signal processing for DNA sequence clustering

Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data

On DNA numerical representations for genomic similarity computation

<div><p>Genomic signal processing (GSP) refers to the use of signal processing for the analysis of genomic data. GSP methods require the transformation or mapping of the genomic data to a numeric representation. To date, several DNA numeric representations (DNR) have been proposed; however, it is not clear what the properties of each DNR are and how the selection of one will affect the results when using a signal processing technique to analyze them. In this paper, we present an experimental study of the characteristics of nine of the most frequently-used DNR. The objective of this paper is to evaluate the behavior of each representation when used to measure the similarity of a given pair of DNA sequences.</p></div