Examining the Incidence and Clients’ Experiences of Single Session Therapy in Italy: A Feasibility Study

Single session therapy (SST) postulates that one session can be sufficient for a client to take charge of the process and work toward their own solutions. Research has been conducted worldwide corroborating SST assumptions with several mental health conditions, but not in Italy. For the first time in Italy, this paper aims to explore: (1) if the most frequent number of sessions in traditional psychotherapy (not SST) is one (Study 1); and (2) the satisfaction of clients who attend SST services and the number who consider one session sufficient to address their presenting problem (Study 2). In Study 1, the records of 476 voluntary clients referred to three different traditional (not SST) psychological services in the west-central region of Latium, Italy, were retrospectively screened, and the number of sessions attended by each client recorded. In Study 2, 85 consecutive clients who voluntarily asked for SST with seven mental health professionals across Italy received a link to an online ad hoc survey, 1–3 weeks after the consultation, evaluating their experience. Study 1 found that the most frequent number of traditional psychotherapeutic (not intentionally SST) sessions was one (124 out of 476 clients (26%). Study 2 found that 44 out of 85 clients (52%) considered one session to be enough, as they felt better or much better and chose not to attend further sessions. Of those who asked for a second session (41 clients), 33 clients (80.5%) indicated that the first session was not enough and 8 clients (19.5%) wanted to address a new problem. These results converge with previous international studies and provide encouragement for the use of SST in both private and public psychological services to address the demand for timely mental health services in Italy. Further research is needed to support the efficacy of SST and to evaluate its cost-effectiveness.     

SBIRT Training in Social Work Education: Evaluating Change Using Standardized Patient Simulation

A grand challenge for social work is addressing widespread public health problems of alcohol misuse. MSW students (n = 83) received Screening, Brief Intervention, and Referral to Treatment (SBIRT) training through didactic sessions, role plays, and pre–post videotaped standardized patient (SP) interactions. SBIRT knowledge, self-reported practice behaviors, and confidence were assessed at pretest, 30 days, and 6 months posttest. Videos were coded to assess intervention-adherent behaviors. General linear mixed models analyzed changes. Participants demonstrated increased adherence to SBIRT behaviors, and knowledge, skills, and confidence increased posttraining. Findings suggest SBIRT training increases students’ capacity to implement evidence-based interventions designed to reduce alcohol misuse.     

How far from identifiability? A systematic overview of the statistical matching problem in a non parametric framework

Statistical matching consists in estimating the joint characteristics of two variables observed in two distinct and independent sample surveys, respectively. In a parametric setup, ranges of estimates for non identifiable parameters are the only estimable items, unless restrictive assumptions on the probabilistic relationship between the non jointly observed variables are imposed. These ranges correspond to the uncertainty due to the absence of joint observations on the pair of variables of interest. The aim of this paper is to analyze the uncertainty in statistical matching in a non parametric setting. A measure of uncertainty is introduced, and its properties studied: this measure studies the “intrinsic” association between the pair of variables, which is constant and equal to 1/6 whatever the form of the marginal distribution functions of the two variables when knowledge on the pair of variables is the only one available in the two samples. This measure becomes useful in the context of the reduction of uncertainty due to further knowledge than data themselves, as in the case of structural zeros. In this case the proposed measure detects how the introduction of further knowledge shrinks the intrinsic uncertainty from 1/6 to smaller values, zero being the case of no uncertainty. Sampling properties of the uncertainty measure and of the bounds of the uncertainty intervals are also proved.     

Modeling agricultural risk management policies – The implementation of the Income Stabilization Tool in Italy

This paper investigates the potential impact of the income stabilisation tool (IST), currently introduced in the European Common Agricultural Policy to reduce farmers’ income risks using Italian agriculture as case study. The paper extends the existing literature by investigating the effects of two implementation issues: level of aggregation of mutual funds (MF); definition of farmers’ contribution (i.e. premium) to MF. We use a simulation approach based on a FADN panel data set of 3421 farms over a period of 7 years to investigate effects on (i) farm-level income variability, (ii) the expected level and variability of indemnifications at the level of mutual funds and (iii) the distribution of net benefits from this policy instrument across the farm population. We find that the introduction of the IST would lead to a significant reduction of income variability in Italian agriculture. Our results support the establishment of a national mutual fund due to the high volatility of indemnification levels at more disaggregated (e.g. regional or sectoral) levels. In addition, our results propose that farmers’ contribution to mutual funds, i.e. premiums paid, should be modulated according to farm size as this reduces the inequality of the distribution of benefits of such tool within the farm population.     

The problem of being a paradigm: the emergence of neural stem cells as example for “Kuhnian” revolution in biology or misconception of the scientific community?

In a thought experiment we want to test how the emergence of adult neural stem cells could constitute an example for a scientific revolution in the sense of Thomas Kuhn. In his major work, “The structure of scientific revolutions, 3rd edn, University of Chicago Press, Chicago” (Kuhn 1996), the philosopher of science, Thomas Kuhn, states that scientific progress is not a cumulative process, but new theories appear by a rather revolutionary sequence of events. Kuhn built his theory on landmark events taken from chemistry and physics, lacking examples from biology. Beginning with Ramon y Cajal’s famous quote, “no new neurons after birth”, from the early years of the twentieth century, and Reynolds and Weiss’s conflicting finding in 1992 of adult neural stem cells giving rise to new neurons, we will test how the finding of neural stem cells in the adult brain matches with Kuhn’s theory. The pivotal problem of defining a paradigm will be our main focus, since the emergence of adult neural stem cells has been acclaimed by the scientific community as the rebuttal of Ramon y Cajal’s paradigm.     

Conditional tests in a competing risks model

Testing for equality of competing risks based on their cumulative incidence functions (CIFs) or their cause specific hazard rates (CSHRs) has been considered by many authors. The finite sample distributions of the existing test statistics are in general complicated and the use of their asymptotic distributions can lead to conservative tests. In this paper we show how to perform some of these tests using the conditional distributions of their corresponding test statistics instead (conditional on the observed data). The resulting conditional tests are initially developed for the case of k = 2 and are then extended to k > 2 by performing a sequence of two sample tests and by combining several risks into one. A simulation study to compare the powers of several tests based on their conditional and asymptotic distributions shows that using conditional tests leads to a gain in power. A real life example is also discussed to show how to implement such conditional tests.     

Reversible jump and the label switching problem in hidden Markov models

Reversible jump Markov chain Monte Carlo (RJMCMC) algorithms can be efficiently applied in Bayesian inference for hidden Markov models (HMMs), when the number of latent regimes is unknown. As for finite mixture models, when priors are invariant to the relabelling of the regimes, HMMs are unidentifiable in data fitting, because multiple ways to label the regimes can alternate during the MCMC iterations; this is the so-called label switching problem. HMMs with an unknown number of regimes are considered here and the goal of this paper is the comparison, both applied and theoretical, of five methods used for tackling label switching within a RJMCMC algorithm; they are: post-processing, partial reordering, permutation sampling, sampling from a Markov prior and rejection sampling. The five strategies we compare have been proposed mostly in the literature of finite mixture models and only two of them, i.e. rejection sampling and partial reordering, have been presented in RJMCMC algorithms for HMMs. We consider RJMCMC algorithms in which the parameters are updated by Gibbs sampling and the dimension of the model changes in split-and-merge and birth-and-death moves. Finally, an example illustrates and compares the five different methodologies.     

Correspondence Analysis of Cumulative Frequencies Using a Decomposition of Taguchi's Statistic

Taguchi's statistic has long been known to be a more appropriate measure of association for ordinal variables than the Pearson chi-squared statistic. Therefore, there is some advantage in using Taguchi's statistic for performing correspondence analysis when a two-way contingency table consists of one ordinal categorical variable. This article will explore the development of correspondence analysis using a decomposition of Taguchi's statistic.     

The Interplay Between Employee and Firm Customer Orientation: Substitution Effect and the Contingency Role of Performance‐Related Rewards

This paper identifies and explains a potential tension between a firm's emphasis on customer orientation (CO) and the extent to which employees value CO as a success factor for individual performance. Based on self‐determination theory and CO implementation research, the authors propose that firm CO may represent both autonomous and controlled motivations for CO, but that employees’ CO is more strongly linked to individual performance when employees experience solely autonomous motivation. Hence, the authors expect a substitution effect whereby the link between employees’ CO and their performance is weaker when firm CO is high. Furthermore, the authors examine a boundary condition for the previous hypothesis and propose that performance‐contingent rewards have a positive effect on the internalization of the extrinsic motivation stemming from firm CO. Two multilevel studies with 979 employees and 201 top management team members from 132 firms support these hypotheses. Against previous research, these findings offer a new perspective on the effectiveness of CO initiatives, propose employees’ motivational states as the theoretical explanation for the heterogeneity in the link between employee CO and performance, and reappraise the role of performance‐contingent rewards in CO research. Managerial implications for the effective implementation of customer‐oriented initiatives within firms are provided.     

The conditional censored graphical lasso estimator

Statistics and Computing - In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these datasets are subject to...