Triggers of Sick Leave : Epidemiological Studies of Work-Related Factors

Background: In Sweden, the prerequisite for compensation during sick leave is a reduction of work ability due to disease or injury. Perhaps as a result of this, sick leave varies between individuals with the same diagnosis and over time in the population in a way that does not coincide with the variations in population health. This implies that to better understand the social phenomenon that is sick leave we need to look into other factors which may influence the association between disease, illness, sickness and sick leave. Aim: The main aim of this thesis was to identify and estimate the effect of factors at work which influence ill individuals to take sick leave. Methods: All four studies were based on data from the TUFS-project (an acronym in Swedish for “Triggers of sick leave”) which took place at six Swedish workplaces in health care, manufacturing industry, and white-collar office work between 2005 and 2007. A total of 1 430 employees (participation proportion 47%) answered a questionnaire at baseline and were subsequently followed with regard to sick leave for 3-12 months and interviewed during or shortly after taking sick leave. Study I used a cohort design assessing exposure at baseline with a longitudinal follow-up of sick leave, and Studies II-IV used a case-crossover design which included only individuals on sick leave, with each case serving as its’ own control. Exposure was measured in a telephone interview conducted during or shortly after sick leave. Results: In Study I an increased risk of future sick leave was found for individuals with a low level of adjustment latitude, whether measured as the general level of adjustment latitude or as having few different types of adjustment possibilities. This is in line with previous studies of adjustment latitude. However in Study II, the results indicated that many individuals had a stable pattern of exposure to lack of adjustment latitude. Among the 35% with variations in exposure during the two weeks prior to sick leave a decreased risk of sick leave was found on days when the participants were exposed to lack of adjustment latitude. In Study III an increased risk of sick leave was found when individuals had been exposed to problems in the relationship with colleagues or superiors the previous two workdays. Furthermore individuals were more likely to take sick leave when they expected a very stressful work situation during the following workday. In Study IV an increased risk of sick leave was found when the participants expected a lower workload than usual. Conclusion: A possible interpretation of the results from Studies I and II is that adjustment latitude both may capture long-lasting effects of a flexible work environment, and temporary possibilities to adjust work to being absent. The increased risks of sick leave found when having been exposed to problems in workplace relationships and when expecting a stressful work situation or a lower workload than usual (Studies III and IV) may function by lowering the threshold of reduced work ability at which an employee feel the need to take sick leave

Reliability and validity testing of team emergency assessment measure in a distributed team context

Medical multi-professional teams are increasingly collaborating via telemedicine. In distributed team settings, members are geographically separated and collaborate through technology. Developing improved training strategies for distributed teams and finding appropriate instruments to assess team performance is necessary. The Team Emergency Assessment Measure (TEAM), an instrument validated in traditional collocated acute-care settings, was tested for validity and reliability in this study when used for distributed teams. Three raters assessed video recordings of simulated team training scenarios (n = 18) among teams with varying levels of proficiency working with a remotely located physician via telemedicine. Inter-rater reliability, determined by intraclass correlation, was 0.74–0.92 on the TEAM instrument’s three domains of leadership, teamwork, and task management. Internal consistency (Cronbach’s alpha) ranged between 0.89–0.97 for the various domains. Predictive validity was established by comparing scores with proficiency levels. Finally, concurrent validity was established by high correlations, >0.92, between scores in the three TEAM domains and the teams’ overall performance. Our results indicate that TEAM can be used in distributed acute-care team settings and consequently applied in future-directed learning and research on distributed healthcare teams

Work-related psychosocial events as triggers of sick leave - results from a Swedish case-crossover study

<p>Abstract</p> <p>Background</p> <p>Although illness is an important cause of sick leave, it has also been suggested that non-medical risk factors may influence this association. If such factors impact on the period of decision making, they should be considered as triggers. Yet, there is no empirical support available.</p> <p>The aim was to investigate whether recent exposure to work-related psychosocial events can trigger the decision to report sick when ill.</p> <p>Methods</p> <p>A case-crossover design was applied to 546 sick-leave spells, extracted from a Swedish cohort of 1 430 employees with a 3-12 month follow-up of new sick-leave spells. Exposure in a case period corresponding to an induction period of one or two days was compared with exposure during control periods sampled from workdays during a two-week period prior to sick leave for the same individual. This was done according to the matched-pair interval and the usual frequency approaches. Results are presented as odds ratios (OR) with 95% confidence intervals (CI).</p> <p>Results</p> <p>Most sick-leave spells happened in relation to acute, minor illnesses that substantially reduced work ability. The risk of taking sick leave was increased when individuals had recently been exposed to problems in their relationship with a superior (OR 3.63; CI 1.44-9.14) or colleagues (OR 4.68; CI 1.43-15.29). Individuals were also more inclined to report sick on days when they expected a very stressful work situation than on a day when they were not under such stress (OR 2.27; CI 1.40-3.70).</p> <p>Conclusions</p> <p>Exposure to problems in workplace relationships or a stressful work situation seems to be able to trigger reporting sick. Psychosocial work-environmental factors appear to have a short-term effect on individuals when deciding to report sick.</p

Is There an Association between Long-Term Sick Leave and Disability Pension and Unemployment beyond the Effect of Health Status? – A Cohort Study

Background: Studies have shown that long-term sick leave is a strong predictor of disability pension. However, few have aimed to disentangle the effect of sick leave and of health status. The objective of this study was to investigate whether there is an association between long-term sick leave and disability pension and unemployment, when taking health status into account. Methods/Principal Findings: The study was based on the Stockholm Public Health Cohort, restricted to 13,027 employed individuals (45.9 % men) aged 18–59 in 2002 and followed until 2007. Hazard ratios (HR) with 95 % Confidence Interval (CI) were estimated by Cox regression models adjusting for socio-demographic factors and five measures of health status. Having been on long-term sick leave increased the risk of disability pension (HR 4.01; 95 % CI 3.19–5.05) and longterm unemployment (HR 1.45; 95 % CI 1.05–2.00), after adjustment for health status. The analyses of long-term sick leave due to specific illness showed that the increased risk for long-term unemployment was confined to the group on sick leave due to musculoskeletal (HR 1.70 95 % CI 1.00–2.89) and mental illness (HR 1.80 95 % CI 1.13–2.88) and further that there was an increased risk for short-term unemployment in the group on sick leave due to mental illness (HR1.57 95%CI 1.09–2.26). Conclusions/Significance: Long-term sick leave increases the risks of both disability pension and unemployment even when taking health status into account. The results support the hypothesis that long-term sick leave may start a process o

Generative models of limit order books

In this thesis generative models in machine learning are developed with the overall aim to improve methods for algorithmic trading on high-frequency electronic exchanges based on limit order books. The thesis consists of two papers. In the first paper a new generative model for the dynamic evolution of a limit order book, based on recurrent neural networks, is developed. The model captures the full dynamics of the limit order book by decomposing the probability of each transition of the limit order book into a product of conditional probabilities of order type, price level, order size, and time delay. Each such conditional probability is modeled by a recurrent neural network. In addition several evaluation metrics for generative models related to order execution are introduced. The generative model is successfully trained to fit both synthetic data generated by a Markov model and real data from the Nasdaq Stockholm exchange. The second paper explores reinforcement learning methods to find optimal policies for trading execution in Markovian models. A number of different approaches are implemented and compared, including a baseline time-weighted average price (TWAP) strategy, tabular Q-learning, and deep Q-learning based on predefined features as well as with the entire limit order book as input. The results indicate that it is preferable to use deep Q-learning with the entire limit order book as input to design efficient execution policies. In order to improve the understanding of the decisions taken by the agent, the learned action-value function for the deep Q-learning with predefined features is visualized as a function of selected features.  I denna avhandling utvecklas generativa modeller i maskininlärning med syfte att förbättra metoder för algoritmisk handel på högfrekventa elektroniska marknader baserat på orderböcker. Avhandlingen består av två artiklar. Den första artikeln utvecklar en generativ modell för den dynamiska utvecklingen av en orderbok baserad på rekurrenta neurala nätverk. Modellen fångar orderbokens fullständiga dynamik genom att bryta ned sannolikheten för varje förändring av orderboken i en produkt av betingade sannolikheter för ordertyp, prisnivå, orderstorlek och tidsfördröjning. Var och en av de betingade sannolikheterna modelleras med ett rekurrent neuralt nätverk.  Dessutom introduceras flera evalueringsmetoder för generativa modeller relaterade till orderexekvering. Den generativa modellen tränas framgångsrikt både för syntetisk data, genererad av en Markovmodell, och riktig data från Nasdaq Stockholm. Den andra artikeln utforskar förstärkningsinlärning för att hitta optimala strategier för orderexekvering i Markovska modeller. Flera olika metoder implementeras och jämförs, inklusive en referensstrategi med tidsviktat medelpris, tabulär Q-inlärning och djup Q-inlärning baserade både på fördefinierade statistikor och med hela orderboken som indata. Resultaten indikerar att det är fördelaktigt att använda hela orderboken som indata för djup Q-inlärning. För att förbättra förståelsen för besluten som agenten tar, visualiseras Q-funktionen för djup Q-inlärning som funktion av de fördefinierade statistikorna.

”Even if it won’t belike any other family” : A qualitative study on the views of foster families

The aim of this study was to gain a better understanding of possible foster families’ views on fostering a child and what they consider to be important factors for deciding whether or not to become a foster family. The studys result is based on five semi-structured interviews with persons whom have an interest for becoming foster families and the findings were analyzed by using a norm theory. The way that the interviewed people described their views on fostering a child could in the analysis be seen as a result of different norms and they often described the ideal foster family based on either their own family or the nuclear family

Image Classification Using a Combination of Convolutional Layers and Restricted Boltzmann Machines

Denna studie har till syfte att undersöka vilken effekt restricted Boltzmann machines (RBMs) har när de kombineras med ett convolutional neural network (CNN) som används för bildklassificering. Detta är ett intressant område som kombinerar övervakad och oövervakad träning av neurala nätverk och som ännu inte har granskats ordentligt. Olika versioner av neurala nätverk tränades och testades med hjälp av två dataset bestående av 70 000 handskrivna siffror respektive 60 000 naturliga bilder. Utgångspunkten var ett vanligt CNN där första lagret sedan byttes ut mot två olika sorters RBMs. För att evaluera effekten av RBMs jämfördes felprocent och träningstid. Resultaten visar att kombinationen av RBMs och CNNskan fungera om rätt implementerad och användas i tillämpningar. Det finns fortfarande mycket kvar att undersöka, då denna studie begränsades av den tillgängliga beräkningskraften.This study aims to investigate what impact restricted Boltzmann machines (RBMs) have when combined with a convolutional neural network (CNN) used for image classification. This is an interesting area of research which combines supervised and unsupervised training of neural networks and it has not been thoroughly examined yet. Different versions of neural networks were trained and tested using two datasets consisting of 70 000 handwrittendigits and 60 000 natural images. The starting point was aregular CNN where the first layer then was replaced by two different kinds of RBMs. To evaluate the effect of RBMs the error rates and training times were compared. The results show that the combination of RBMs and CNNs can work if implemented right and can be used in different applications. There is still much left to investigate, since this study was limited by the available computational power

Generative models of limit order books

In this thesis generative models in machine learning are developed with the overall aim to improve methods for algorithmic trading on high-frequency electronic exchanges based on limit order books. The thesis consists of two papers. In the first paper a new generative model for the dynamic evolution of a limit order book, based on recurrent neural networks, is developed. The model captures the full dynamics of the limit order book by decomposing the probability of each transition of the limit order book into a product of conditional probabilities of order type, price level, order size, and time delay. Each such conditional probability is modeled by a recurrent neural network. In addition several evaluation metrics for generative models related to order execution are introduced. The generative model is successfully trained to fit both synthetic data generated by a Markov model and real data from the Nasdaq Stockholm exchange. The second paper explores reinforcement learning methods to find optimal policies for trading execution in Markovian models. A number of different approaches are implemented and compared, including a baseline time-weighted average price (TWAP) strategy, tabular Q-learning, and deep Q-learning based on predefined features as well as with the entire limit order book as input. The results indicate that it is preferable to use deep Q-learning with the entire limit order book as input to design efficient execution policies. In order to improve the understanding of the decisions taken by the agent, the learned action-value function for the deep Q-learning with predefined features is visualized as a function of selected features.  I denna avhandling utvecklas generativa modeller i maskininlärning med syfte att förbättra metoder för algoritmisk handel på högfrekventa elektroniska marknader baserat på orderböcker. Avhandlingen består av två artiklar. Den första artikeln utvecklar en generativ modell för den dynamiska utvecklingen av en orderbok baserad på rekurrenta neurala nätverk. Modellen fångar orderbokens fullständiga dynamik genom att bryta ned sannolikheten för varje förändring av orderboken i en produkt av betingade sannolikheter för ordertyp, prisnivå, orderstorlek och tidsfördröjning. Var och en av de betingade sannolikheterna modelleras med ett rekurrent neuralt nätverk.  Dessutom introduceras flera evalueringsmetoder för generativa modeller relaterade till orderexekvering. Den generativa modellen tränas framgångsrikt både för syntetisk data, genererad av en Markovmodell, och riktig data från Nasdaq Stockholm. Den andra artikeln utforskar förstärkningsinlärning för att hitta optimala strategier för orderexekvering i Markovska modeller. Flera olika metoder implementeras och jämförs, inklusive en referensstrategi med tidsviktat medelpris, tabulär Q-inlärning och djup Q-inlärning baserade både på fördefinierade statistikor och med hela orderboken som indata. Resultaten indikerar att det är fördelaktigt att använda hela orderboken som indata för djup Q-inlärning. För att förbättra förståelsen för besluten som agenten tar, visualiseras Q-funktionen för djup Q-inlärning som funktion av de fördefinierade statistikorna.

Evaluering av massivt skalbara Gaussiska processer

Gaussian process methods are flexible non-parametric Bayesian methods used for regression and classification. They allow for explicit handling of uncertainty and are able to learn complex structures in the data. Their main limitation is their scaling characteristics: for n training points the complexity is O(n³) for training and O(n²) for prediction per test data point. This makes full Gaussian process methods prohibitive to use on training sets larger than a few thousand data points. There has been recent research on approximation methods to make Gaussian processes scalable without severely affecting the performance. Some of these new approximation techniques are still not fully investigated and in a practical situation it is hard to know which method to choose. This thesis examines and evaluates scalable GP methods, especially focusing on the framework Massively Scalable Gaussian Processes introduced by Wilson et al. in 2016, which reduces the training complexity to nearly O(n) and the prediction complexity to O(1). The framework involves inducing point methods, local covariance function interpolation, exploitations of structured matrices and projections to low-dimensional spaces. The properties of the different approximations are studied and the possibilities of making improvements are discussed.  Gaussiska processmetoder är flexibla icke-parametriska Bayesianska metoder som används för regression och klassificering. De tillåter explicit hantering av osäkerhet och kan lära sig komplexa strukturer i data. Den största begränsningen är deras skalningsegenskaper: för n träningspunkter är komplexiteten O(n³) för träning och O(n²) för prediktion per ny datapunkt. Detta gör att kompletta Gaussiska processer är för krävande föratt använda på träningsdata större än några tusen datapunkter. Det har nyligen forskats på approximationsmetoder för att göra Gaussiska processer skalbara utan att påverka prestandan allvarligt. Några av dessa nya approximationsstekniker är fortfarande inte fullkomligt undersökta och i en praktisk situation är det svårt att veta vilken metod man ska använda. Denna uppsats undersöker och utvärderar skalbara GP-metoder, särskilt med fokus på ramverket Massivt Skalbara Gaussiska Processer introducerat av Wilson et al. 2016, vilket minskar träningskomplexiteten till O(n) och prediktionskomplexiteten till O(1). Ramverket innehåller inducerande punkt-metoder, lokal kärninterpolering, utnyttjande av strukturerade matriser och projiceringar till lågdimensionella rum. Egenskaperna hos de olika approximationerna studeras och möjligheterna att göra förbättringar diskutera