Estructura de control y sus correlatos en una burocracia profesional: el caso de un hospital

Resumen

Se utilizó el método del «Gráfico de Control» de Tannenbaum con el fin de estudiar la estructura de poder y autoridad de una organización hospitalaria, los resultados obtenidos confirman el carácter de burocracia profesional característico de este tipo de organizaciones. Se detectó una disminución del control percibido de la línea administrativa por parte del componente más profesionalizado. Así mismo se demostró la percepción diferencial de la estructura de control por parte de los miembros en función de su localización estructural y sus efectos sobre el contexto diseño y eficacia de los puestos y unidades, confirmándose los resultados de otros estudios que han detectado este mismo fenómeno. Estos resultados apoyan la bondad «Gráfico de Control» para el estudio de la estructura de poder y autoridad en las organizaciones formales.     

Older women's cardiovascular responses to deep-water running

The aim of the study was to examine submaximal and maximal physiological responses and perceived exertion during deep-water running with a vest compared with the responses during treadmill running in healthy elderly women. Eleven healthy women 70 +/- 2 years old participated. On two different occasions they performed a graded maximal exercise test on a treadmill on land and a graded maximal exercise test in water wearing a vest. At maximal work the oxygen uptake was 29% lower (p < .05), the heart rate was 8% lower (p < .05), and the ventilation was 16% lower (p < .05) during deep-water running than during treadmill running. During submaximal absolute work the heart rate was higher during deep-water running than during treadmill running for the elderly women. The participants had lower maximal oxygen uptake, heart rate, ventilation, respiratory-exchange ratio, and rate of perceived exertion during maximal deep-water running with a vest than during maximal treadmill running. These responses were, however, higher during submaximal deep-water running than during treadmill running.     

Ergonomics, automation and logistics: practical and effective combination of working methods, a case study of a baking company

The aim of this study was to combine three different analytical methods from three different disciplines to diagnose the ergonomic conditions, manufacturing and supply chain operation of a baking company. The study explores a summary of comprehensive working methods that combines the ergonomics, automation and logistics study methods in the diagnosis of working conditions and productivity. The participatory approach of this type of study that involves the feelings and first-hand knowledge of workers of the operation are determining factors in defining points of action and ergonomic interventions, as well as defining opportunities in the automation of manufacturing and logistics, to cope with the needs of the company. The study identified an ergonomic situation (high prevalence of wrist-hand pain), and the combination of interdisciplinary techniques applied allowed to improve this condition in the company. This type of study allows a primary basis of the opportunities presented by the combination of specialized methods of different disciplines, for the definition of comprehensive action plans for the company. Additionally, it outlines opportunities for improvement and recommendations to mitigate the burden associated with occupational diseases and as an end result improve the quality of life and productivity of workers.     

A method for combining inference across related nonparametric Bayesian models

Summary.  We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In the nonparametric context, modelling is complicated by the fact that the random quantities over which we define the hierarchy are infinite dimensional. We discuss a formal definition of such a hierarchical model. The approach includes a regression at the level of the nonparametric model. For the special case of Dirichlet process mixtures, we develop a Markov chain Monte Carlo scheme to allow efficient implementation of full posterior inference in the given model.     

A randomized intervention trial to reduce mechanical exposures in the Colombian flower industry

Evidence on the effectiveness of ergonomic interventions to reduce mechanical demands and upper-extremity MSDs is scarce in agriculture. We conducted an intervention to reduce mechanical exposures during manual flower cutting through job rotation, education and reduction of force requirements. One-hundred and twenty workers (20 to 60 years old; 89% women) from six companies that cultivate roses participated in this study. Three companies were randomly assigned to control and intervention groups. We studied changes between baseline and follow-up in self-reported effort and upper-extremity postures, kinematics and muscular activity. Most of the observed changes were moderate for both groups. The intervention group showed differential improvements compared to the control group for the maximum wrist radial deviation and forearm pronation, and acceleration of the forearm supination-pronation and elbow flexion-extension; and the muscular activity of the flexor and extensor carpi radialis and the flexor carpi ulnaris. However, we also observed that the maximum ulnar deviation, velocity of the wrist flexion-extension and muscular activity of the extensor carpi ulnaris improved more in the control group. These mixed results may be related to limited time for intervention adjustment, and uncontrolled task changes in the control group. Future research should address these issues and test other solutions.     

Nonparametric Bayesian modelling using skewed Dirichlet processes

We introduce a new class of discrete random probability measures that extend the definition of Dirichlet process (DP) by explicitly incorporating skewness. The asymmetry is controlled by a single parameter in such a way that symmetric DPs are obtained as a special case of the general construction. We review the main properties of skewed DPs and develop appropriate Polya urn schemes. We illustrate the modelling in the context of linear regression models of the capital asset pricing model (CAPM) type, where assessing symmetry for the error distribution is important to check validity of the model.     

Density regression using repulsive distributions

Flexible regression is a traditional motivation for the development of non-parametric Bayesian models. A popular approach for this involves a joint model for responses and covariates, from which the desired result arises by conditioning on the covariates. Many such models involve the convolution of a continuous kernel with some discrete random probability measure defined as an infinite mixture of i.i.d. atoms. Following this strategy, we propose a flexible model that involves the concept of repulsion between atoms. We show that this results in a more parsimonious representation of the regression than the i.i.d. counterpart. The key aspect is that repulsion discourages mixture components that are near each other, thus favouring parsimony. We show that the conditional model retains the repulsive features, thus facilitating interpretation of the resulting flexible regression, and with little or no sacrifice of model fit compared to the infinite mixture case. We show the utility of the methodology by way of a small simulation study and an application to a well-known data set.     

Nonparametric Bayesian inference in applications

Nonparametric Bayesian (BNP) inference is concerned with inference for infinite dimensional parameters, including unknown distributions, families of distributions, random mean functions and more. Better computational resources and increased use of massive automated or semi-automated data collection makes BNP models more and more common. We briefly review some of the main classes of models, with an emphasis on how they arise from applied research questions, and focus in more depth only on BNP models for spatial inference as a good example of a class of inference problems where BNP models can successfully address limitations of parametric inference.