Self-Stigma of seeking help and job burnout in mental health care providers: the comparative study of Lithuanian and the USA samples

This study aimed to explore the correlation between job burnout and self-stigma of seeking help among nonmedical mental health care providers (psychologists, social workers, and counselors) in two countries – Lithuania and the US. The study included 234 professionals (111 social workers and 123 psychologists) from Lithuania and 93 professionals (33 counselors, 23 social workers, and 37 psychologists) from the US on a voluntary basis (93% females, mean age – 39.81?years). They completed a self-reported questionnaire with the Self-Stigma of Seeking Help Scale (SSOSH) and the Maslach Burnout Inventory-General Survey (MBI-GS). The results revealed a statistically significant positive correlation between self-stigmatization and burnout in the Lithuanian sample, but only weak positive correlation between depersonalization and self-stigma of seeking help in the US sample. The relationship between the self-stigma of seeking help and burnout was stronger in the Lithuanian sample of professionals when compared to their colleagues in the US.     

A Spatial-temporal Model for Temperature with Seasonal Variance

We propose a spatial-temporal stochastic model for daily average surface temperature data. First, we build a model for a single spatial location, independently on the spatial information. The model includes trend, seasonality, and mean reversion, together with a seasonally dependent variance of the residuals. The spatial dependency is modelled by a Gaussian random field. Empirical fitting to data collected in 16 measurement stations in Lithuania over more than 40 years shows that our model captures the seasonality in the autocorrelation of the squared residuals, a property of temperature data already observed by other authors. We demonstrate through examples that our spatial-temporal model is applicable for prediction and classification.     

Analysis and modelling of wind speed in New York

In this paper we propose an ARMA time-series model for the wind speed at a single spatial location, and estimate it on in-sample data recorded in three different wind farm regions in New York state. The data have a three-hour granularity, but based on applications to financial wind derivatives contracts, we also consider daily average wind speeds. We demonstrate that there are large discrepancies in the behaviour of daily average and three-hourly wind speed records. The validation procedure based on out-of-sample observations reflects that the proposed model is reliable and can be used for various practical applications, like, for instance, weather prediction, pricing of financial wind contracts, wind generated power, etc. Furthermore, we discuss some striking resemblances with temperature dynamics.     

Spatial–temporal model for wind speed in Lithuania

In this paper, we propose a spatial–temporal model for the wind speed (WS). We first estimate the model at the single spatial meteorological station independently on spatial correlations. The temporal model contains seasonality, a higher-order autoregressive component and a variance describing the remaining heteroskedesticity in residuals. We then model spatial dependencies by a Gaussian random field. The model is estimated on daily WS records from 18 meteorological stations in Lithuania. The validation procedure based on out-of-sample observations shows that the proposed model is reliable and can be used for various practical applications.     

What is mixed in welfare mix? Welfare ideologies at stake in the Lithuanian case of social service delivery

The article deals with the development of social welfare and social services in Lithuania by describing cultural contexts and disclosing evaluations of social service providers and recipients and needs of social service delivery. The authors of the article discuss the changing role of the state, pose a question as to what the institutions and the actors are and to what extent should they participate in the creation of social welfare. One of the aims of this paper is to describe the development of the welfare mix in Lithuania, by showing that welfare is inevitably woven into the historical, economic, political and social context; the distinctive cultural configuration of the welfare mix in social services delivery in Lithuania is revealed. Empirical research (survey of social services recipients and providers) presented in the article discloses that actors of social services delivery experience tension and ambiguity between the demand of neoliberal policy to choose and be responsible and the expectation that the state will participate in the social service market. At the same time social services recipients and providers express a need for different actors to take an active part in the welfare system.     

Before,during and after crossing the border: narrating the ‘visa-free Grodno’

On 26 October 2016, Belarus introduced a visa-free border crossing regime for travellers visiting the Augustow Canal area and the city of Grodno. Based on a multi-method study, this article aims at investigating how this ‘visa-free regime’ is enacted and ‘narrated’ before, during and after border crossing. First, it analyses the website ‘grodnovisafree.by’ and searches for patterns of what the visitors encounter before travelling to the border area. Second, it analyses the border crossing experience, based on a participant observation while crossing the border from Lithuania to Belarus. Third, it investigates the linguistic and multimodal landscape after crossing the border in the city of Grodno. This study concludes that the ‘real’ ‘narrative’ about challenges and boundaries that the visitors have to overcome to cross the border is at times overshadowed by the ‘ideal’ picture of easily accessible Grodno and its area.     

Particle learning for Bayesian semi-parametric stochastic volatility model

This article designs a Sequential Monte Carlo (SMC) algorithm for estimation of Bayesian semi-parametric Stochastic Volatility model for financial data. In particular, it makes use of one of the most recent particle filters called Particle Learning (PL). SMC methods are especially well suited for state-space models and can be seen as a cost-efficient alternative to Markov Chain Monte Carlo (MCMC), since they allow for online type inference. The posterior distributions are updated as new data is observed, which is exceedingly costly using MCMC. Also, PL allows for consistent online model comparison using sequential predictive log Bayes factors. A simulated data is used in order to compare the posterior outputs for the PL and MCMC schemes, which are shown to be almost identical. Finally, a short real data application is included.