Risk: For Whom? Representations of Mining Activity by Different Social Actors in the Molango Manganese District of Hidalgo,Mexico

Previous studies have shown high levels of manganese exposure and neurocognitive damage in the population living in the mining zone in Molango, Mexico. One of the objectives of the Intersectoral Group on Environmental Management for the mining district has been to provide public participation in the risk management plan. To achieve this, it is important to know how the different social actors represent the mining activity. The objectives of this study were to characterize the social representations of the mining activity by different social actors. A qualitative design was used based on in‐depth interviews of residents, public officials, and a mining company representative. The analysis was conducted according to themes for each group of actors. Essentially, distinct social representations of the different mining activities were identified. Residents viewed mining activities as synonymous with contamination and, therefore, as having affected all areas of their environment, health, and daily life. These activities were seen as a collective risk. The public officials and the mining company held that there was no evidence of harm and saw mining activities as a generator of regional development. Harm to health and the environment were seen as a stance taken by the communities in order to obtain economic benefits from the company. These images of the “other” are shaped by social, political, and cultural factors. They make it difficult for the actors to reach cooperative agreements and thereby affect progress on the risk management plan. Decisionmakers need to take these differences into account when promoting social participation.     

Système de soins et mortalité régionale: une analyse causale

Nous examinons l'impact du système de soins sur les différences régionales de mortalité adulte en Espagne, en distinguant l'offre et la demande de soins. Nous avons utilisé un modèle d'analyse des covariances, ajusté aux données par le logiciel LISREL 7. La distinction faite entre l'offre et la demande de soins conduit à observer un impact spatial significatif de l'utilisation du système de soins sur la mortalité des adultes. Hormis le cas des accidents, le signe de la relation est toutefois opposé à celui prévu! Une meilleure spécification du modèle permettrait peut-être de modifier cette conclusion.We examine the impact of the health care system on regional adult mortality differences in Spain, distinguishing between the demand and supply of health care. We have used a covariance analysis model, fitted to the data using LISREL 7. The distinction made between supply and demand of medical care leads to a significant spatial impact of the use of the health care system on adult mortality. Except for accidents, however, the sign of the relation is opposite to what is expected! Better specification of the model could possibly reverse this conclusion.     

The Equivalence of Two Proportions Revisited

The classic conditional test for checking that the difference between two independent proportions is not null may not be appropriate in many circumstances. Dunnett & Gent (1977) showed that in clinical trials, in studies of drugs, etc, the aim is to prove the practical equality (equivalence) of both proportions. On other occasions the aim may be the opposite: i.e. to prove that the two proportions are substantially different (biologically significant). Both cases are usually solved by two one-sided tests (TOST test). In this article, this procedure is shown to be conservative and two true two-sided tests for each case are proposed.     

Robustness issues under imprecise beliefs and preferences

Robust Bayesian analysis has been rapidly developing, specially in terms of sensitivity to the prior. We argue that these studies are insufficient in decision-making contexts. Consequently, we discuss what robustness issues should be of interest in those situations and provide answers in some important cases.     

Issues in psychotherapy with HIV-infected Latinos in New York city

This article will discuss issues important in psychotherapy with Latinos with HIV/AIDS in New York City. The various levels of oppressed group membership will be discussed as they relate to a therapeutic understanding of the mental health needs of Latinos. A case study will be used to demonstrate the clinical applications of the Model of Multiple Oppression.     

Small-Sample Comparisons for Goodness-of-Fit Statistics in One-Way Multinomials with Composite Hypotheses

The small-sample behaviour of power-divergence goodness-of-fit statistics with composite hypotheses was evaluated with multinomial models of up to five cells and up to three parameters. Their performance was assessed by comparing asymptotic test sizes with exact test sizes obtained by enumeration in the near right tail, where 1-?∈?(0.90,?0.95], and in the far right tail, where 1-?∈?(0.95,?0.99]. The study addressed all combinations of power-diparse JAS312HH01.sgmvergence estimates of indices ν?∈?{-1/2,?0,?1/3,?1/2,?2/3,?1,?3/ 2} and power-divergence statistics of indices λ?∈?{-1/2,?0,?1/3,?1/2,?2/3,?1,?3/2}. The results indicate that the asymptotic approximation is sufficiently accurate (by the criterion that the average exact size is no larger than ±10% of the nominal asymptotic test size) in the near right tail when ν=0 and λ=1/2, and in the far right tail when ν=0 and λ=1/3, in both cases providing that the smallest expectation in the composite hypothesis exceeds 5. The only exception to this rule is the case of models that render a near-equiprobable composite hypothesis on the boundaries of the parameter space, where average exact sizes are usually quite different from nominal sizes despite the fact that the smallest expectation in these conditions is usually well above 5.     

Unemployment insurance: Theoretical approaches and empirical evidence

This article analyses the principal theoretical approaches to the effects on the labour market when an unemployment insurance scheme is introduced. The article also reviews the international data derived from such insurance schemes already in use in some European and American economies. The principal conclusion of this study is that an unemployment scheme does not necessarily affect employment decisions adversely, nor does it encourage long-term unemployment. Furthermore, to the extent that this analysis goes beyond the traditional limits of the employment-unemployment relationship and considers the institutional aspects, which can be at least as important as the individual benefits, the effects on employment and on the allocation of resources in the economy can be positive. Finally, the study identifies some criteria to be considered when designing the scheme, such as the level of benefits, the period for which they are granted, the type of beneficiary covered, the type of financing and its relationship to the quality of the information and its complementary role in the process of adapting the labour force to new demands.     

New critical region tables for Fisher's exact test

The best-known non-asymptotic method for comparing two independent proportions is Fisher's exact text. The usual critical region (CR) tables for this test contain one or more of the following defects:they distinguish between rows and columns; they distinguish between the alternatives H = p1 < p2 and H = p1 > p2; they assume that the error for the two-tailed test is twice that of the one-tailed test; they do not use the optimal version of the test; they do not give both CRs for one and two tails at the same time. All this results in the unnecessary duplication of the space required for the tables, the construction of tables of low-powered methods, or the need to manipulate two different tables (one for the one-tailed test, the other for the two-tailed test). This paper presents CR tables which have been obtained from the most powerful version of Fisher's exact test and which occupy the minimum space possible. The tables, which are valid for one- or two-tailed tests, have levels of significance of 10%, 5% and 1% and values for N (the total size of both samples) of less than or equal to 40. This article shows how to calculate the P value in a specific problem, using the tables as a means of partial checking and as a preliminary step to determining the exact P value.