The effect of reimbursement fee changes on service production for laboratory tests in Norwegian primary health care

This paper examines how changes in reimbursement fees influence the service production of laboratory tests among Norwegian primary care physicians. The data represent a panel of 2,083 physicians paid on a fee-for-service basis for the period 2001–04. We construct a variable that measures the exogenous effect of changes in reimbursement fees on physician income. We measure service production by the number of laboratory tests per consultation, the relative change in the composition of laboratory tests, and the number of tests per consultation ordered from clinical laboratories. There are three main findings. First, physicians reduce the number of laboratory tests per consultation when fees decrease. Second, physicians change the composition of laboratory tests to tests that are more expensive when fees decrease. Finally, there is a spillover effect to the specialist health care sector because physicians who experience an income loss for tests analysed at the office laboratory order more tests from clinical laboratories. The results imply that fee regulation may be a simple means of controlling government expenditure. However, it is important to note the change in composition along with the potential spillover effects to other parts of the health care sector to obtain a complete picture of the influence of fee regulation on physician behaviourPhysician reimbursement; laboratory tests; financial incentives; income effect; substitution effect

Intentional transparency : a rhetorical case study of BP's transparency policy throughout the Deepwater Horizon crisis in comparison to how Tesco dealt with the european horsmeat scandal

Masteroppgave i samfunnskommunikasjon – Universitetet i Agder 2014In this thesis, I have studied how a major event such as a crisis changes the way a company promotes transparency in their corporate communication. To gain knowledge about this aspect of their communication I did a multicase study of two cases; BP and the Deepwater Horizon accident and Tesco and the horsemeat scandal. Methodically I did a categorization of press releases from three selected periods. One year before the accident, during the accident and one year after the accident. This categorization was followed by email interviews as well as a rhetorical analysis of selected press releases from the two companies. The primary findings of this thesis are divergent between the two organisations. BP’s press releases did not seem to change significantly considering the transparency shown in their press releases. Tesco’s press releases showed a significant change in how they promoted transparency in the area affected by the crisis

Police reform and community policing in Kenya: the bumpy road from policy to practice

A reform is underway in Kenya, aimed at transforming the police organization into a people- centred police service. Among other things, this involves enhancing police-public trust and partnerships through community policing (COP). Two state-initiated COP models have been implemented: the National Police Service’s Community Policing Structure, and the Nyumba Kumi model of the President's Office. On paper, police reform and the two COP models would appear to have the potential to improve police-public cooperation. In practice, however, implementation has proven difficult. Interviews and meetings with local community organizations, community representatives and police officers in urban and rural parts of Kenya indicate that scepticism towards the two COP models is common, as is refusal to engage in them. But why is this so? Why are these two COP models unsuccessful in enhancing police-public trust and cooperation? This article analyses how various contextual factors -such as conflicting socio-economic and political interests at the community and national levels, institutional challenges within the police, the overall role and mandate of the police in Kenya, and a top-down approach to COP- impede the intended police paradigm shift

Fine-Grained News Classification

This thesis investigates the concepts of fine-grained news classification. To do this, an empirical study in which human annotators categorized news was conducted. The also study consisted of measuring agreement between hu- man annotators and evaluating the precision of the annotations. The study revealed a need for a framework for fine-grained news classification. A framework was then developed and evaluated, producing a complete annotated dataset.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Ny forståelse av gasshydratfenomener og naturlige inhibitorer i råoljesystemer gjennom massespektrometri og maskinlæring

Gas hydrates represent one of the main flow assurance issues in the oil and gas industry as they can cause complete blockage of pipelines and process equipment, forcing shut downs. Previous studies have shown that some crude oils form hydrates that do not agglomerate or deposit, but remain as transportable dispersions. This is commonly believed to be due to naturally occurring components present in the crude oil, however, despite decades of research, their exact structures have not yet been determined. Some studies have suggested that these components are present in the acid fractions of the oils or are related to the asphaltene content of the oils. Crude oils are among the worlds most complex organic mixtures and can contain up to 100 000 different constituents, making them difficult to characterise using traditional mass spectrometers. The high mass accuracy of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) yields a resolution greater than traditional techniques, making FT-ICR MS able to characterise crude oils to a greater extent, and possibly identify hydrate active components. FT-ICR MS spectra usually contain tens of thousands of peaks, and data treatment methods able to find underlying relationships in big data sets are required. Machine learning and multivariate statistics include many methods suitable for big data. A literature review identified a number of promising methods, and the current status for the use of machine learning for analysis of gas hydrates and FT-ICR MS data was analysed. The literature study revealed that although many studies have used machine learning to predict thermodynamic properties of gas hydrates, very little work have been done in analysing gas hydrate related samples measured by FT-ICR MS. In order to aid their identification, a successive accumulation procedure for increasing the concentrations of hydrate active components was developed by SINTEF. Comparison of the mass spectra from spiked and unspiked samples revealed some peaks that increased in intensity over the spiking levels. Several classification methods were used in combination with variable selection, and peaks related to hydrate formation were identified. The corresponding molecular formulas were determined, and the peaks were assumed to be related to asphaltenes, naphthenes and polyethylene glycol. To aid the characterisation of the oils, infrared spectroscopy (both Fourier Transform infrared and near infrared) was combined with FT-ICR MS in a multiblock analysis to predict the density of crude oils. Two different strategies for data fusion were attempted, and sequential fusion of the blocks achieved the highest prediction accuracy both before and after reducing the dimensions of the data sets by variable selection. As crude oils have such complex matrixes, samples are often very different, and many methods are not able to handle high degrees of variations or non-linearities between the samples. Hierarchical cluster-based partial least squares regression (HC-PLSR) clusters the data and builds local models within each cluster. HC-PLSR can thus handle non-linearities between clusters, but as PLSR is a linear model the data is still required to be locally linear. HC-PLSR was therefore expanded into deep learning (HC-CNN and HC-RNN) and SVR (HC-SVR). The deep learning-based models outperformed HC-PLSR for a data set predicting average molecular weights from hydrolysed raw materials. The analysis of the FT-ICR MS spectra revealed that the large amounts of information contained in the data (due to the high resolution) can disturb the predictive models, but the use of variable selection counteracts this effect. Several methods from machine learning and multivariate statistics were proven valuable for prediction of various parameters from FT-ICR MS using both classification and regression methods.Gasshydrater er et av hovedproblemene for Flow assurance i olje- og gassnæringen ettersom at de kan forårsake blokkeringer i oljerørledninger og prosessutstyr som krever at systemet må stenges ned. Tidligere studier har vist at noen råoljer danner hydrater som ikke agglomererer eller avsetter, men som forblir som transporterbare dispersjoner. Dette antas å være på grunn av naturlig forekommende komponenter til stede i råoljen, men til tross for årevis med forskning er deres nøyaktige strukturer enda ikke bestemt i detalj. Noen studier har indikert at disse komponentene kan stamme fra syrefraksjonene i oljen eller være relatert til asfalteninnholdet i oljene. Råoljer er blant verdens mest komplekse organiske blandinger og kan inneholde opptil 100 000 forskjellige bestanddeler, som gjør dem vanskelig å karakterisere ved bruk av tradisjonelle massespektrometre. Den høye masseoppløsningen Fourier-transform ion syklotron resonans massespektrometri (FT-ICR MS) gir en høyere oppløsning enn tradisjonelle teknikker, som gjør FT-ICR MS i stand til å karakterisere råoljer i større grad og muligens identifisere hydrataktive komponenter. FT-ICR MS spektre inneholder vanligvis titusenvis av topper, og det er nødvendig å bruke databehandlingsmetoder i stand til å håndtere store datasett, med muligheter til å finne underliggende forhold for å analysere spektrene. Maskinlæring og multivariat statistikk har mange metoder som er passende for store datasett. En litteratur studie identifiserte flere metoder og den nåværende statusen for bruken av maskinlæring for analyse av gasshydrater og FT-ICR MS data. Litteraturstudien viste at selv om mange studier har brukt maskinlæring til å predikere termodynamiske egenskaper for gasshydrater, har lite arbeid blitt gjort med å analysere gasshydrat relaterte prøver målt med FT-ICR MS. For å bistå identifikasjonen ble en suksessiv akkumuleringsprosedyre for å øke konsentrasjonene av hydrataktive komponenter utviklet av SINTEF. Sammenligninger av massespektrene fra spikede og uspikede prøver viste at noen topper økte sammen med spikingnivåene. Flere klassifikasjonsmetoder ble brukt i kombinasjon med ariabelseleksjon for å identifisere topper relatert til hydratformasjon. Molekylformler ble bestemt og toppene ble antatt å være relatert til asfaltener, naftener og polyetylenglykol. For å bistå karakteriseringen av oljene ble infrarød spektroskopi inkludert med FT-ICR MS i en multiblokk analyse for å predikere tettheten til råoljene. To forskjellige strategier for datafusjonering ble testet og sekvensiell fusjonering av blokkene oppnådde den høyeste prediksjonsnøyaktigheten både før og etter reduksjon av datasettene med bruk av variabelseleksjon. Ettersom råoljer har så kompleks sammensetning, er prøvene ofte veldig forskjellige og mange metoder er ikke egnet for å håndtere store variasjoner eller ikke-lineariteter mellom prøvene. Hierarchical cluster-based partial least squares regression (HCPLSR) grupperer dataene og lager lokale modeller for hver gruppe. HC-PLSR kan dermed håndtere ikke-lineariteter mellom gruppene, men siden PLSR er en lokal modell må dataene fortsatt være lokalt lineære. HC-PLSR ble derfor utvidet til convolutional neural networks (HC-CNN) og recurrent neural networks (HC-RNN) og support vector regression (HC-SVR). Disse dyp læring metodene utkonkurrerte HC-PLSR for et datasett som predikerte gjennomsnittlig molekylvekt fra hydrolyserte råmaterialer. Analysen av FT-ICR MS spektre viste at spektrene inneholder veldig mye informasjon. Disse store mengdene med data kan forstyrre prediksjonsmodeller, men bruken av variabelseleksjon motvirket denne effekten. Flere metoder fra maskinlæring og multivariat statistikk har blitt vist å være nyttige for prediksjon av flere parametere from FT-ICR MS data ved bruk av både klassifisering og regresjon

Police Reform and Community Policing in Kenya: The Bumpy Road from Policy to Practice

A reform is underway in Kenya, aimed at transforming the police organization into a people- centred police service. Among other things, this involves enhancing police-public trust and partnerships through community policing (COP). Two state-initiated COP models have been implemented: the National Police Service’s Community Policing Structure, and the Nyumba Kumi model of the President’s Office. On paper, police reform and the two COP models would appear to have the potential to improve police-public cooperation. In practice, however, implementation has proven difficult. Interviews and meetings with local community organizations, community representatives and police officers in urban and rural parts of Kenya indicate that scepticism towards the two COP models is common, as is refusal to engage in them. But why is this so? Why are these two COP models unsuccessful in enhancing police-public trust and cooperation? This article analyses how various contextual factors—such as conflicting socio-economic and political interests at the community and national levels, institutional challenges within the police, the overall role and mandate of the police in Kenya, and a top-down approach to COP—impede the intended police paradigm shift

Undrained triaxial test stress path shape as an indicator of sample quality

publishedVersio

Considering Head Variations in a Linear Model for Optimal Hydro Scheduling

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