Emergency Workers’ Quality of Life: The Protective Role of Sense of Community, Efficacy Beliefs and Coping Strategies

This study, involving a sample of 764 emergency workers, investigates dimensions of quality of life at work (Compassion fatigue, Burnout and Compassion satisfaction), and their relationships with Coping strategies and some psychosocial variables (Sense of Community, Collective Efficacy and Self-efficacy). Results indicate the usefulness of distinguishing between positive and negative indicators of emergency workers’ quality of life. Compassion satisfaction is positively correlated with efficacy beliefs, Sense of Community and the use of Active coping strategies. Burnout and Compassion fatigue are especially correlated with the use of dysfunctional coping strategies like distraction and self-criticism. Volunteer emergency workers enjoy a higher well being than full-time professional workers. Results and their implications for interventions aimed at increasing rescue workers’ quality of life by enhancing psychosocial competences are discussed.     

Paradigmatic experiments: The Dictator Game

Recent experiments with the Dictator Game (and the ensuing discussions) have been affected by considerable confusion regarding the purpose of this design. A common complaint is that the design gives rise to fragile regularities and therefore is of little use for theory-testing. We take issue with this view, and instead argue that the Dictator Game is potentially a very useful tool for experimental game theory, if properly used. It is particularly useful for investigating social norms, but economists have failed to take advantage of the Dictator Game because they still lack an adequate theory of norms.     

About the “economic” origin of altruism

The principal aim of this paper is to present an evolutionary model based on a simple inequality system which shows how altruism can increase exchanges of goods and services, in order to study the conditions which can permit the emergence and prevalence of altruistic behaviors. We will show that given certain conditions, and even without considering group selection, kin selection or reciprocal altruism, altruists may have a greater probability of survival than egoists.     

LIQUIDITY AND INEFFICIENT INVESTMENT

We study consumer liquidity in a general equilibrium model where the friction is the nonpledgeability of future income. Liquidity helps to overcome the absence of a double coincidence of wants. Consumers over‐hoard liquidity and the resulting competitive equilibrium is constrained inefficient. Fiscal policy following a large negative shock can increase ex‐ante welfare. If the government cannot commit, the ex‐post optimal fiscal policy will be too small from an ex‐ante perspective. The model throws light on the holding of foreign reserves in international markets.     

Promoting Self Determination for Persons with Developmental Disabilities

As community inclusion efforts continue to develop, the recognition of the capacity and rights of persons with cognitive and learning difficulties to participate in planning their own programs has been increasingly acknowledged by providers. This paper presents a needs assessment which was conducted with a small group of people with disabilities, to ascertain their perspectives on community integration for the purpose of social and recreational activity. Anchored on acknowledgment of the rights of people with disabilities to exercise self determination, a focus group methodology was used to provide the opportunity for the client group to have a voice in developing their own recreation opportunities.     

Cultural Access and Mental Health: An Exploratory Study

Loss of well-being, rising rates of depression and various psychological illnesses are a public health concern. This study aims to explore the associations between cultural access and mental illness. Applying a panel methodology to the 20 Italian regions (2002–2007 period) the relationship between public health expenditure per-capita (the dependent variable) and hospital discharge rates for different illnesses (the regressors) has been estimated. Then the impact of the private spending in culture (the regressors) on the mental illness—as approximated by the discharge rate for mental illness—(the dependent variable), has been verified via generalised method of moments (GMM) approach. Data came from the National Census of the Italian Institute of Statistics (ISTAT). The results of empirical analysis have highlighted how the discharge rate for mental illness is substantially improved by some forms of cultural consumptions. The coefficients associated with unemployment rates (the control variables) have a positive impact on mental illness, thus suggesting that unemployment has an impact on the discharge rate for mental illness; the greatest impact is observed for the long-term unemployment rate. The results are beneficial for designing and implementing preventive strategies to reduce mental illness and public health expenditure by considering the impact of cultural access.     

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.     

Extensions of Permutation Solutions to Test for Treatment Effects in Replicated Single-Case Alternation Experiments with Multivariate Response

Single-case experiments are frequently used to do research involving a clinical intervention, since large-n trials are often impractical in clinical research. In order to investigate a possible difference in the effect of the treatments considered in the study, nonparametric instruments are valid tools; in particular, permutation solutions work well when we wish to assess differences in treatment effects. We present an extension of a permutation solution to the multivariate response case and to the case of replicated single-case experiments. A simulation study shows that the approach is both reliable under the null hypothesis and powerful under the alternative. At the end, we present the results of an application to two real experiments.     

A Permutation Solution to Test for Treatment Effects in Alternation Design Single-Case Experiments

Research involving a clinical intervention is normally aimed at testing the treatment effects on a dependent variable, which is assumed to be a relevant indicator of health or quality-of-life status. In much clinical research large-n trials are in fact impractical because the availability of individuals within well-defined categories is limited in this application field. This makes it more and more important to concentrate on single-case experiments. The goal with these is to investigate the presence of a difference in the effect of the treatments considered in the study. In this setting, valid inference generally cannot be made using the parametric statistical procedures that are typically used for the analysis of clinical trials and other large-n designs. Hence, nonparametric tools can be a valid alternative to analyze this kind of data. We propose a permutation solution to assess treatment effects in single-case experiments within alternation designs. An extension to the case of more than two treatments is also presented. A simulation study shows that the approach is both reliable under the null hypothesis and powerful under the alternative, and that it improves the performance of a considered competitor. In the end, we present the results of a real case application.