Examining Gambling-Related Crime Reports in the National Finnish Police Register

The aim of this study is to examine the connection between gambling and criminal activity in the National Finnish Police Register. First, a method was created that enabled the search for gambling-related police reports in the National Finnish Police Register. The method is based on finding gambling-related police reports by using gambling-related headwords. Second, all police reports from 2011 that included any mention of gambling were read through (n = 2,233). Suspected gambling-related of crimes (n = 737) were selected from these reports. Those suspected gambling-related crimes were then described and categorized into six different categories: suspected online-related crimes; suspected crimes that were related to lifestyle-gaming; suspected crimes that involved a gambler as a victim of a crime; criminal activity related to problem gambling; casino-connected crimes, and intimate partnership violence resulting from gambling problems. This study, being the first in Finland, generated information on the connection between gambling and criminal activity from the perspective of police reports. Moreover, the study highlights methodological issues that are involved in studying police reports.     

Scenarios, Uncertainty and Conditional Forecasts of the World Population

Current official population forecasts differ little from those that Whelpton made 50 years ago either in the cohort–component methodology used or in the arguments used to motivate the assumptions. However, Whelpton produced some of the most erroneous forecasts of this century. This suggests that current forecasters should ensure that they give users an assessment of the uncertainty of their forecasts. We show how simple statistical methods can be combined with expert judgment to arrive at an overall predictive distribution for the future population. We apply the methods to a world population forecast that was made in 1994. Accepting that point forecast, we find that the probability is only about 2% that the world population in the year 2030 will be less than the low scenario of 8317 million. The probability that the world population will exceed the high scenario of 10 736 million is about 13%. Similarly, the probability is only about 51% that the high–low interval of a recent United Nations (UN) forecast will contain the true population in the year 2025. Even if we consider the UN high–low intervals as conditional on the possible future policies of its member states, they appear to have a relatively small probability of encompassing the future population.     

Job-strain and well-being among Finnish social workers: exploring the differences in occupational well-being between child protection social workers and social workers without duties in child protection

Work-related mental distress and its impact on employees’ working life is a mounting issue among Finnish social workers. This article focuses on identifying the factors associated with child welfare social workers’ occupational well-being. The occupational well-being of Finnish child welfare social workers (N?=?364) and social workers whose duties do not include child protection work (N?=?524) was explored and compared with each other using t-test statistics and logistic regression analysis. The data, collected in 2014/2015, were obtained from an ongoing longitudinal cohort study on work-related well-being among Finnish public sector employees. A multi-dimensional and holistic approach to occupational well-being was used as the outline for the analysis and comparison of the two groups. Child protection social workers reported higher levels of burnout and secondary traumatic stress than social workers without child protection duties. Despite these burdens, both groups showed a similar level of general health, compassion satisfaction and overall occupational well-being. Individual and organizational factors associated with high occupational well-being were identified. Supervision was found to be an important supporting element. This study identified multiple determinants related to social workers’ occupational well-being, comprising positive and negative elements with regard to organizational and individual factors.     

Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data

We present an algorithm for multivariate robust Bayesian linear regression with missing data. The iterative algorithm computes an approximative posterior for the model parameters based on the variational Bayes (VB) method. Compared to the EM algorithm, the VB method has the advantage that the variance for the model parameters is also computed directly by the algorithm. We consider three families of Gaussian scale mixture models for the measurements, which include as special cases the multivariate t distribution, the multivariate Laplace distribution, and the contaminated normal model. The observations can contain missing values, assuming that the missing data mechanism can be ignored. A Matlab/Octave implementation of the algorithm is presented and applied to solve three reference examples from the literature.     

Unavoidable regularities in long words with bounded number of symbol occurrences

Traditionally in combinatorics on words one studies unavoidable regularities that appear in sufficiently long strings over a fixed size alphabet. Inspired by permutation problems originating from information security, another viewpoint is taken in this paper. We focus on combinatorial properties of long words in which the number of occurrences of any symbol is restricted by a fixed given constant. More precisely, we show that for all positive integers m and q there exists the least positive integer N(m,q) which is smaller than $m^{2^{q-1}}$ and satisfies the following: If α is a word such that

1. |alph(α)|≥N(m,q) (i.e., the cardinality of the alphabet of α is at least N(m,q)); and
2. |α| _a ≤q for each a∈alph(α) (i.e., the number of occurrences of any symbol of alph(α) in α is at most q),

then there exist a set A?alph(α) of cardinality |A|=m, an integer p∈{1,2,…,q}, and permutations σ ₁,σ ₂,…,σ _p :{1,2,…,m}→{1,2,…,m} for which $$\pi_A(\alpha)\in a_{\sigma_1(1)}^+\cdots a_{\sigma_1(m)}^+a_{\sigma _2(1)}^+\cdots a_{\sigma_2(m)}^+\cdots a_{\sigma_p(1)}^+\cdots a_{\sigma_p(m)}^+ .$$ Here A={a ₁,a ₂,…,a _m } and π _A is the projection morphism from alph(α)^? into A ^?. The second part of the paper considers information security. We give an introduction to (generalized iterated) hash functions and their security properties; finally we demonstrate how our combinatorial results are connected to constructing multicollision attacks on these functions.     

The Use of Chemical Restraints for Older Long-Term Hospital Patients: A Case Report from Finland

The purpose of this study was to describe the use and concomitant use of psychotropics and other drugs as chemical restraints in the aged in long-term hospital care. The study consisted of 154 patients (42 men, 112 women) hospitalized in five long-term care wards in Pori City Hospital, Finland. Three or more psychotropics were regularly given to 33% of the patients and regularly or irregularly to 53% of the patients. Two or more benzodiazepine derivatives or related drugs were regularly given to 24% of the patients and regularly or irregularly to 46% of the patients. The very poor cognitive and functional abilities of the patients, the common concomitant use of psychotropic drugs, the use of psychotropics to control the behavior of the patients, and the lack of documentation of the effects and side effects of the drugs give rise to the conclusion that psychotropics were used as chemical restraints in these long-term care wards.     

Emergence of Industries: A Review and Future Directions

This paper reviews the literature on the emergence of industries and the theoretical and methodological approaches employed. The analysis reveals that industry emergence can be depicted as a three‐stage process. In the first, initial stage, a disruption to the existing industrial order triggers the second, the co‐evolutionary stage, which includes four sub‐processes related to developments in technology, markets, activity networks and industry identity. The convergence of these sub‐processes leads to the third stage, a growth stage and the birth of a new industry. While these three stages and the four sub‐processes are well covered in the literature, the authors find that there is a lack of understanding in terms of the transitions between the stages, the interactions and interdependencies between sub‐processes and moderating factors of industry emergence. Future research can bridge these gaps by exploring the different origins and initial conditions of industries, the processes and interactions in the earliest stages of industry emergence, and the role of facilitating and managing industry emergence. This implies a shift in the research focus from the industries that have emerged to the nascent processes of emergence.     

Error models for official mortality forecasts

"The Office of the Actuary, U.S. Social Security Administration, produces alternative forecasts of mortality to reflect uncertainty about the future.... In this article we identify the components and assumptions of the official forecasts and approximate them by stochastic parametric models. We estimate parameters of the models from past data, derive statistical intervals for the forecasts, and compare them with the official high-low intervals. We use the models to evaluate the forecasts rather than to develop different predictions of the future. Analysis of data from 1972 to 1985 shows that the official intervals for mortality forecasts for males or females aged 45-70 have approximately a 95% chance of including the true mortality rate in any year. For other ages the chances are much less than 95%."     

Effects of targets and aggregation on the propagation of error in mortality forecasts

"Official forecasts of mortality depend on assumptions about target values for the future rates of decline in mortality rates. Smooth functions connect the jump-off (base-year) mortality to the level implied by the targets. Three alternative sets of targets are assumed, leading to high, middle, and low forecasts. We show that this process can be closely modeled using simple linear statistical models. These explicit models allow us to analyze the error structure of the forecasts. We show that the current assumption of perfect correlation between errors in different ages, at different forecast years, and for different causes of death, is erroneous. An alternative correlation structure is suggested, and we show how its parameters can be estimated from the past data. The effect of the level of aggregation on the accuracy of mortality forecasts is considered." The geographical focus is on the United States. (SUMMARY IN FRE)     

Study Design in Causal Models

The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing‐data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions on whether a causal or observational relationship can be estimated from the collected incomplete data can be made directly from the graph. Causal models with design offer a systematic and unifying view to scientific inference and increase the clarity and speed of communication. Examples on the causal models for a case–control study, a nested case–control study, a clinical trial and a two‐stage case–cohort study are presented.