Sammanfattning Titel: Samma fast olika – En explorativ studie om hur undersköterskorna inom hemtjänstenhanterar äldreomsorgens värdegrund och de organisatoriska förutsättningarna Författare: Patrik Bengtsson och Viktor Bergerus Uppsatsen syftade till att med en explorativ ansats, undersöka hur undersköterskor ihemtjänsten hanterar målen om självbestämmande, delaktighet och individanpassning samtvilka faktorer som möjliggör respektive begränsar implementeringen av målen. För att studienssyfte skulle uppnås, användes en kvalitativ metod. Fem intervjuer har genomförts medundersköterskor som arbetar inom hemtjänsten i fyra olika kommuner. Resultatet frånintervjuerna tolkades utifrån grundad teori som valdes då syftet var explorativt. Under analysengenererades koder, underkategorier, kategorier som resulterade i en main concern. Det fannsolika faktorer som influerade när det kom till vad som främjade respektive hämmade införandetav värdegrunden i det dagliga arbetet. Det begrepp eller main concern som framkom varnormdiskrepans, vilket i korthet innebär att arbetet som utförs ska vara flexibelt men genomförsinom en rigid inramning. Nyinstitutionell användes för att diskutera resultatet, slutsatsen var attundersköterskornas förmåga att anpassa sig är det som får hemtjänstens arbete att fungera trotsmotstridiga mål och krav.  What do you want to do ?New mailCopy<img /> / Abstract Title: Same but different - An exploratory study on how nursing aides within the homecare handlethe values of elderly care and the organizational conditions Authors: Patrik Bengtsson och Viktor Bergerus The thesis aimed to, with an exploratory approach, examine how nursing aides in the home careservice manage the goals of self-determination, participation, and individualization, as well aswhich factors enable and limit the implementation of these goals. To achieve the purpose of thestudy, a qualitative method was used. Five interviews have been conducted with nurses workingin home care in four different municipalities. The results of the interviews were interpretedbased on grounded theory which was chosen when the purpose was exploratory. During theanalysis, codes, subcategories and categories were generated that resulted in a main concern.There were various factors that influenced when it comes to what promotes and inhibits theintroduction of the values in daily work. The concept or main concern that emerged was thenorm discrepancy, which in short means that the work performed must be flexible but be carriedout within a rigid framework. Neo-institutional theory was used to discuss the results, theconclusion was that the nursing aides’ ability to adapt is what makes the home care service'swork despite conflicting goals and requirements. What do you want to do ?New mailCopy<img /> / <p></p><p>What do you want to do ?New mailCopy</p>

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<p dir="ltr">Generating images from training samples solves the challenge of imbalanced data. It provides the necessary data to run machine learning algorithms for image classification, anomaly detection, and pattern recognition tasks. In medical settings, having imbalanced data results in higher false negatives due to a lack of positive samples. Generative Adversarial Networks (GANs) have been widely adopted for image generation. GANs allow models to train without computing intractable probability while producing high-quality images. However, evaluating GANs has been challenging for the researchers due to a need for an objective function. Most studies assess the quality of generated images and the variety of classes those images cover. Overfitting of training images, however, has received less attention from researchers. When the generated images are mere copies of the training data, GAN models will overfit and will not generalize well. This study examines the ability to detect overfitting of popular metrics: Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID). We investigate the metrics on two types of data: handwritten digits and chest x-ray images using Analysis of Variance (ANOVA) models.</p>

Majority of millennials are daily users of Instagram and in conjunction with previous studies on Instagram displaying negative effects on psychological well-being, how individuals perceive their identity in relation to their Instagram use is interesting as it could be a part of how their psychological wellbeing is affected through use. Most previous research on Instagram and psychological well-being are general and based on quantitative methods. Descriptive findings on how individuals relate to their Instagram in terms of image or identity is not yet explored. The research question of this paper is therefore to investigate if there exists a perceived ideal image on Instagram and if individuals separate their Instagram image from their perceived real identities. Semi-structured interviews with 11 participants categorized through thematic analysis indicate a perceived ideal image on Instagram exists, and majority of participants cannot separate their Instagram image from their perceived real identity. It is assumed through findings that the level of awareness when applying one’s image or identity during Instagram use could indicate how risky it is for individuals to be affected negatively on their psychological well-being during usage. Increasing awareness of how one identify him- or herself based on his or her image or perceived real self could potentially decrease the risk of negative social comparison and self-discrepancy in interaction with one’s Instagram use. Findings showed there exists a perceived ideal image on Instagram while there is no coherent perception among participants in how they separate their perceived image from their perceived real identity. Future research could investigate whether this ’identity incongruence’ while using Instagram is part of a new way of constructing one’ identity in a world where virtual and real no longer has clear borders. / Majoriteten av Millennials är dagliga användare av Instagram och i kombination med tidigare studier om Instagram som tyder på negativa effekter på det psykosociala välmåendet - är hur individer upplever deras identitet i relation till deras Instagram-användande är intressant att undersöka, då det kan vara del av hur deras psykosociala välmående påverkas genom användning. Större delen av tidigare studier om Instagram och psykosocialt välmående är generell i naturen och baserat på kvantitativa metoder. Detaljerade resultat av hur individer relaterar till deras Instagram i form av image eller identitet är ännu inte undersökt. Frågeställningen är därför att undersöka om en upplevd idealisk image existerar på Instagram och om individer separerar deras Instagram image från deras upplevda riktiga identitet. Semi-strukturerade intervjuer med elva medverkande, kategoriserade genom tematisk analys, indikerar på att en upplevd idealisk image på Instagram existerar samt att majoriteten av dem medverkande inte kan skilja deras Instagram image från deras upplevda riktiga identitet. Det är antaget genom resultatet att nivån av medvetenhet man har i appliceringen av ens image eller identitet under användningen av Instagram kan indikera hur riskfyllt det är för individer att bli negativt påverkade psykosocialt under deras användning. Genom ökat medvetande av hur man identifierar sig själv baserat på sin image eller upplevda riktiga jag skulle risken för negativ social jämförelse och själv-diskrepans potentiellt minska i interaktion med ens Instagram-användande. Resultaten visar att en upplevd idealisk image på Instagram existerar samtidigt som en sammanhängande uppfattning av hur de medverkande separerar deras upplevda image från deras upplevda riktiga identitet inte existerar. Framtida studier kan undersöka närmare om denna ’identitets-inkongruens’ som uppstår under Instagram-användande är ett nytt sätt att skapa sin identitet på i en värld där det virtuella och det riktiga inte längre har tydliga gränser.

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The advent of Next Generation Sequencing (NGS), a little over a decade ago, has led to a vast and rapid increase in the generation of genomic data. The drastically reduced cost has in turn enabled powerful modifications that can be used to investigate not just genetic, but epigenetic, phenomena. Epigenetics refers to the study of mechanisms effecting gene expression other than the genetic code itself and thus, at the transcription level, incorporates DNA methylation, transcription factor binding and histone modifications amongst others. This thesis outlines and tackles two major challenges in the computational analysis of such data using techniques from machine learning. Firstly, I address the problem of testing for differential methylation between groups of bisulfite sequencing data sets. DNA methylation plays an important role in genomic imprinting, X-chromosome inactivation and the repression of repetitive elements, as well as being implicated in numerous diseases, such as cancer. Bisulfite sequencing provides single nucleotide resolution methylation data at the whole genome scale, but a sensitive analysis of such data is difficult. I propose a solution that uses a powerful kernel-based machine learning technique, the Maximum Mean Discrepancy, to leverage well-characterised spatial correlations in DNA methylation, and adapt the method for this particular use. I use this tailored method to analyse a novel data set from a study of ageing in three different tissues in the mouse. This study motivates further modifications to the method and highlights the utility of the underlying measure as an exploratory tool for methylation analysis. Secondly, I address the problem of predictive and explanatory modelling of chromatin immunoprecipitation sequencing data (ChIP-Seq). ChIP-Seq is typically used to assay the binding of a protein of interest, such as a transcription factor or histone, to the DNA, and as such is one of the most widely used sequencing assays. While peak callers are a powerful tool in identifying binding sites of sparse and clean ChIPSeq profiles, more broad signals defy analysis in this framework. Instead, generative models that explain the data in terms of the underlying sequence can help uncover mechanisms that predicting binding or the lack thereof. I explore current problems with ChIP-Seq analysis, such as zero-inflation and the use of the control experiment, known as the input. I then devise a method for representing k-mers that enables the use of longer DNA sub-sequences within a flexible model development framework, such as generalised linear models, without heavy programming requirements. Finally, I use these insights to develop an appropriate Bayesian generative model that predicts ChIP-Seq count data in terms of the underlying DNA sequence, incorporating DNA methylation information where available, fitting the model with the Expectation-Maximization algorithm. The model is tested on simulated data and real data pertaining to the histone mark H3k27me3. This thesis therefore straddles the fields of bioinformatics and machine learning. Bioinformatics is both plagued and blessed by the plethora of different techniques available for gathering data and their continual innovations. Each technique presents a unique challenge, and hence out-of-the-box machine learning techniques have had little success in solving biological problems. While I have focused on NGS data, the methods developed in this thesis are likely to be applicable to future technologies, such as Third Generation Sequencing methods, and the lessons learned in their adaptation will be informative for the next wave of computational challenges.