Bakgrund: Caring (vårdande vård) är kärnan inom nursing (omvårdnad) och det sker i mötet mellan sjuksköterska och patient. Det bygger på en intim, personlig relation som på många sätt påminner och en vänskapsrelation dock skiljer sig denna relation från en autentisk vänskap då det ingår i sjuksköterskans ansvar att sörja för patienten. Att sörja för patienten på ett caring sätt och samtidigt upprätthålla en professionell distans är svårt och sjuksköterskor kan bli för engagerade med exempelvis utbrändhet som följd. Hur detta ska undvikas utan att förlora caring är dock inte utrett. Balansakten mellan närhet och distans är således komplicerad. Syfte: Att beskriva hur sjuksköterskan hanterar balansakten mellan närhet och distans i vårdandet. Metod: En allmän litteraturöversikt med kvalitativ ansats där tio vetenskapliga artiklar analyserats med inspiration från innehållsanalys. Resultat: Tre teman framkom med tillhörande underteman; Inre förutsättningar; att bygga på kunskap, att välja sin egen väg och att acceptera situationen, Gränssättningar; att skydda självet och att skifta mellan roller och Yttre förutsättningar; att ha tillgång till support, att ha tid att vårda, att ta hänsyn till kontext och att patienten bestämmer nivån. Slutsats: God balans är inte något statiskt där ett tillvägagångssätt gäller för alla. Sjuksköterskor lär sig, genom erfarenhet och självkännedom, var gränsen för det personliga engagemanget går för just dem. Sjuksköterskan som är villig inför och förberedd på den emotionella investeringen arbetet innefattar är mer benägen att klara av det. Klinisk betydelse: Studien bidrar till ökad förståelse för sjuksköterskans balansakt mellan närhet och distans samt påvisar vikten av effektiv handledning då autentisk vårdande vård oundvikligt är en emotionell investering för sjuksköterskan. / Background: Caring is the essence of nursing and it occurs in the encounter between nurse and patient. It is built on an intimate, friendship like relationship but it differs from an authentic friendship in as much as it is the nurse’s assignment to care for the patient. To truly care for the patient and at the same time uphold a professional distance is difficult and nurses can become over-involved that can lead to burn outs. How to avoid over-involvement, without losing caring, is not as yet undisputed. The act of balancing between closeness and distance is thus complicated. Aim: To describe how nurses deal with the act of balancing between closeness and distance in caring. Method: A qualitative literary review of ten scientific articles analyzed with inspiration from content analysis. Findings: Three themes emerged with associated sub themes; Internal conditions; to build on knowledge, to choose your own path and to accept the situation, Setting limits; to protect the self and to switch between roles and External conditions; to have access to support, to have the time to care, to have regard for context and that the patient determines the level. Conclusion: Good balance is not something static where one truth serves all. Nurses learn, through experience and self-knowledge where to draw their personal line regarding emotional involvement. The nurse who is willing and prepared for the emotional investment that comes with the job is more likely to endure it. Clinical meaning: This study increases understanding of the act of balancing between closeness and distance that the nurse exercises in her or his work and underlines the need for effective tutoring as authentic caring is unavoidably an emotional investment for the nurse.

The amount of data is continually growing and the ability to efficiently search through vast amounts of data is almost always sought after. To efficiently find data in a set there exist many technologies and methods but all of them cost in the form of resources like cpu-cycles, memory and storage. In this study a search engine (SE) is optimized using several methods and techniques. Thesis looks into how to optimize a SE that is based on an external revision database.The optimized implementation is compared to a non-optimized implementation when executing a query. An artificial neural network (ANN) trained on a dataset containing 3 years normal usage at a company is used to prioritize within the resultset before returning the result to the caller. The new indexing algorithms have improved the document space complexity by removing all duplicate documents that add no value. Machine learning (ML) has been used to analyze the user behaviour to reduce the necessary amount of documents that gets retrieved by a query.

A fundamental goal of statisticians is to make inferences from the sample about characteristics of the underlying population. This is an inverse problem, since we are trying to recover a feature of the input with the availability of observations on an output. Towards this end, we consider complexity penalized methods, because they balance goodness of fit and generalizability of the solution. The data from the underlying population may come in diverse formats - structured or unstructured - such as probability distributions, text tokens, or graph characteristics. Depending on the defining features of the problem we can chose the appropriate complexity penalized approach, and assess the quality of the estimate produced by it. Favorable characteristics are strong theoretical guarantees of closeness to the true value and interpretability. Our work fits within this framework and spans the areas of simulation optimization, text mining and network inference. The first problem we consider is model calibration under the assumption that given a hypothesized input model, we can use stochastic simulation to obtain its corresponding output observations. We formulate it as a stochastic program by maximizing the entropy of the input distribution subject to moment matching. We then propose an iterative scheme via simulation to approximately solve it. We prove convergence of the proposed algorithm under appropriate conditions and demonstrate the performance via numerical studies. The second problem we consider is summarizing text documents through an inferred set of topics. We propose a frequentist reformulation of a Bayesian regularization scheme. Through our complexity-penalized perspective we lend further insight into the nature of the loss function and the regularization achieved through the priors in the Bayesian formulation. The third problem is concerned with the impact of sampling on the degree distribution of a network. Under many sampling designs, we have a linear inverse problem characterized by an ill-conditioned matrix. We investigate the theoretical properties of an approximate solution for the degree distribution found by regularizing the solution of the ill-conditioned least squares objective. Particularly, we study the rate at which the penalized solution tends to the true value as a function of network size and sampling rate.

Children with medical complexity (CMC) require increased number and length of hospitalizations, and increased need for care coordination.1-3 Many complex children with neuromuscular diseases have scoliosis, or a deformity of the spine. Often, scoliosis in these patients affects multiple organ systems and requires spinal fusion surgery to repair the deformity and decrease the likelihood of further organ damage.4,5 While it is well-known that spinal fusion surgery is costly and a high-risk procedure, little research has evaluated the perioperative process of spinal fusion patients. Furthermore, few care pathways exist for medically complex patients undergoing spinal fusions.6 This study examines the pre-, peri-, and post-operative experiences of families of patients undergoing spinal fusion surgery at Boston Children’s Hospital. Providers, organization, leadership and teamwork, and overall outcomes are assessed by this qualitative study. Initial data suggest that a pathway improving coordination and communication, especially among interactions with the surgical coordinator, should be implemented to improve scheduling of surgery and appointments throughout the perioperative process

La croissance des données disponibles aujourd'hui génère de nouvelles problématiques pour lesquelles l'apprentissage statistique ne possède pas de réponses adaptées. Ainsi le cadre classique de la classification qui consiste à affecter une ou plusieurs classes à une instance est étendu à des problèmes avec des milliers, voire des millions de classes différentes. Avec ces problèmes viennent de nouveaux axes de recherches comme \deleted{le temps} \added{la réduction de la compléxité} de classification qui est habituellement linéaire en fonction du nombre de classes du problème\deleted{.} \added{, ce qui est problématique lorsque le nombre de classe devient trop important.} Plusieurs familles de solutions pour cette problématique ont émergé comme la construction d'une hiérarchie de classifieurs ou bien l'adaptation de méthodes ensemblistes de type ECOC. Le travail présenté ici propose deux nouvelles méthodes pour répondre au problème de classification extrême. Le premier travail consiste en une nouvelle mesure asymétrique pour le partitionnement de classes dans le cadre d'une classification hiérarchique alors que le second axe explore l'élaboration d'un algorithme séquentiel actif d'agrégation des classifieurs les plus intéressants. / The increase in volume of the data nowadays is at the origin of new problematics for which machine learning does not possess adapted answers. The usual classification task which requires to assign one or more classes to an example is extended to problems with thousands or even millions of different classes. Those problems bring new research fields like the complexity reduction of the classification process. That classification process has a complexity usually linear with the number of classes of the problem, which can be an issue if the number of classes is too large. Various ways to deal with those new problems have emerged like the construction of a hierarchy of classifiers or the adaptation of ECOC ensemble methods. The work presented here describes two new methods to answer this extreme classification task. The first one consists in a new asymmetrical measure to help the partitioning of the classes in order to build a hierarchy of classes. The second one proposes a sequential way to aggregate effectively the most interesting classifiers.

Dans cette thèse, nous définissons une nouvelle classe de graphes : les graphes hypotriangulés. Les graphes hypotriangulés vérifient que pour tout chemin de longueur deux, il existe une arête ou un autre chemin de longueur deux entre ses extrémités. Cette classe permet par exemple de modéliser des réseaux robustes. En effet, nous montrons que dans de tels graphes, la suppression d'une arête ou d'un sommet ne modifie pas la distance initiale entre toutes paires de sommets non adjacents. Ensuite, nous étudions et démontrons plusieurs propriétés pour cette classe de graphes. En particulier, après avoir introduit une famille de partitions spécifiques, nous montrons les relations entre certains éléments de cette famille et leur caractère hypotriangulé. De plus, grâce à ces partitions, nous caractérisons les graphes hypotriangulés minimum, qui, parmi les graphes hypotriangulés connexes, minimisent le nombre d'arêtes pour un nombre de sommets fixés.Dans une deuxième partie, nous étudions la complexité, pour la classe des graphes hypotriangulés, de problèmes difficiles dans le cas général. Nous montrons d'abord que les problèmes classiques de cycle hamiltonien, coloration, clique maximum et stable maximum restent NP-difficiles pour cette classe de graphes. Ensuite, nous nous intéressons à des problèmes de modification de graphes, pour lesquels il s'agit de déterminer le nombre minimal d'arêtes à ajouter ou supprimer à un graphe pour obtenir un graphe hypotriangulé : nous montrons la complexité de ces problèmes pour plusieurs classes de graphes. / In this thesis, we define a new class of graphs : the hypochordal graphs. These graphs satisfy that for any path of length two, there exists a chord or another path of length two between its two endpoints. This class can represent robust networks. Indeed, we show that in such graphs, in the case of an edge or a vertex deletion, the distance beween any pair of nonadjacent vertices remains unchanged. Then, we study several properties for this class of graphs. Especially, after introducing a family of specific partitions, we show the relations between some of these partitions and hypochordality. Moreover, thanks to these partitions, we characterise minimum hypochordal graph, that are, among connected hypochordal graphs, those that minimise the number of edges for a given number of vertices. In a second part, we study the complexity, for hypochordal graphs, of problems that are NP-hard in the general case. We first show that the classical problems of hamiltonian cycle, colouring, maximum clique and maximum stable set remain NP-hard for this class of graphs. Then, we analyse graph modification problems : deciding the minimal number of edges to add or delete from a graph, in order to obtain an hypochordal graph. We study the complexity of these problems for sevaral classes of graphs.

Machine learning (ML) is an area of computer science that gives computers the ability to learn data patterns without prior programming for those patterns. Using neural networks in this area is based on simulating the biological functions of neurons in brains to learn patterns in data, giving computers a predictive ability to comprehend how data can be clustered. This research investigates the possibilities of using neural networks for classifying email, i.e. working as an email case manager. A Deep Neural Network (DNN) are multiple layers of neurons connected to each other by trainable weights. The main objective of this thesis was to evaluate how the three input arguments - data size, training time and neural network structure – affects the accuracy of Deep Neural Networks pattern recognition; also an evaluation of how the DNN performs compared to the statistical ML method, Naïve Bayes, in the form of prediction accuracy and complexity; and finally the viability of the resulting DNN as a case manager. Results show an improvement of accuracy on our networks with the increase of training time and data size respectively. By testing increasingly complex network structures (larger networks of neurons with more layers) it is observed that overfitting becomes a problem with increased training time, i.e. how accuracy decrease after a certain threshold of training time. Naïve Bayes classifiers performs worse than DNN in terms of accuracy, but better in reduced complexity; making NB viable on mobile platforms. We conclude that our developed prototype may work well in tangent with existing case management systems, tested by future research.

This study focuses on the study of a unique but commonly occurring manufacturing problem of scheduling of customized jobs consisting of two operations on a single multi-purpose machine with the performance objective of minimizing the number of tardy jobs (jobs that are not completed by their due dates). Each customized job to be complete needs one unique operation and one common operation performed on it. We considered a static case in this work. The objective of minimizing the number of tardy jobs is considered where all jobs have equal weights and the maximum tardiness has no effect on the performance. This problem is proved in literature as NP-hard and hence practically very difficult to obtain optimal solution within reasonable computational time. Till date only a pseudo-polynomial algorithm is given to solve this problem with no concrete computational experiments designed to prove the efficiency and working of the algorithm for different problem instances. We propose a heuristic algorithm based on the Moore-Hodgson's algorithm combining with other procedures and optimal schedule properties from the literature to solve this problem. In literature, Moore-Hodgson's algorithm is an efficient heuristic algorithm that minimizes the number of tardy jobs for the classical single machine one-operation problems. The performance of the heuristic is evaluated through extensive computational experiments for large real size data. The obtained results are compared to the solutions obtained by implementing the optimal pseudo-polynomial algorithm and the performance of the heuristic is tested on large data sets. The test data for the computational experiments are generated randomly using MATLAB 6.1. Future directions of research and development on the problem to improve the obtained solution by the heuristic algorithm are given.

Medication complexity and nonadherence are significant risk factors for avoidable hospitalizations and health care spending for older adults in the United States. However, limited empirical research has investigated pharmacist-run telephonic medication management programs as a potential solution to the problem of reducing medication complexity while improving medication adherence. This quantitative study employed the behavioral change model to analyze archival data from a sample of 1,148 participants, examining the relationship of a pharmacist-run telephonic consulting program on medication adherence and medication complexity for one pharmacy benefit management firm's Medicare Part D recipients. The primary research questions investigated the relationship of medication therapy management programs to medication adherence and complexity. Data were assessed using correlation and regression analysis to determine the association between receiving pharmacist counseling, medication adherence, and medication complexity, and to assess the strength of any relationships identified. No linear relationship was found between pharmacists' counseling, medication complexity, and medication adherence. However, the study found a weak correlation between medication complexity and comorbidities, and between medication complexity and medication adherence. This study promotes positive social change by identifying information that can be used to reduce pharmaceutical industry liability by improving proper management of medications, by reducing the burden of comorbidities related to poor management of chronic disease, and streamlining health services and improving their outcom

Military conflicts have become nonlinear and the interrelated political and socio-economic changes within these conflicts have created new challenges for American policymakers. A tool called Wallis' Propositional Analysis (PA) suggests a new paradigm that includes thinking about complexity and robustness/systemicity in a policy. The purpose of this single case study was to determine how the PA paradigm adds heuristic value to complex policy decision-making. A backdrop of Wallerstein's complexity theory and complex adaptive systems (CAS) guided this study. This study examined policy statements from the Obama administration on the Afghanistan and Pakistan conflicts in late December 2009. Data were coded and analyzed using Wallis' specific methodological approach that includes a systematic analysis of the policy's propositions and complexity and robustness/systemicity. Key findings indicated that the PA paradigm offers a heuristic method for how to think about the interrelated propositions within a policy that reflect the expected changes the policy intends to make. Specifically, this study demonstrated that an interwoven PA structural approach to policymaking affords the policymaker a method to consider the complex and nonlinear changes in the policy environment. By applying the PA paradigm, policymakers can positively impact social change by exploring policy options that consider a range of possible outcomes from the policy proposal, prior to policy implementation.