Work Stress in the Nursing Profession : An Evaluation of Organizational Causal Attribution

The goal of this study was to examine the role of organizational causal attribution in understanding the relation of work stressors (work-role overload, excessive role responsibility, and unpleasant physical environment) and personal resources (social support and cognitive coping) to such organizational-attitudinal outcomes as work engagement, turnover intention, and organizational identification. In some analyses, cognitive coping was also treated as an organizational outcome. Causal attribution was conceptualized in terms of four dimensions: internality-externality, attributing the cause of one’s successes and failures to oneself, as opposed to external factors, stability (thinking that the cause of one’s successes and failures is stable over time), globality (perceiving the cause to be operative on many areas of one’s life), and controllability (believing that one can control the causes of one’s successes and failures). Several hypotheses were derived from Karasek’s (1989) Job Demands–Control (JD-C) model and from the Job Demands–Resources (JD-R) model (Demerouti, Bakker, Nachreiner & Schaufeli, 2001). Based on the JD-C model, a number of moderation effects were predicted, stating that the strength of the association of work stressors with the outcome variables (e.g. turnover intentions) varies as a function of the causal attribution; for example, unpleasant work environment is more strongly associated with turnover intention among those with an external locus of causality than among those with an internal locuse of causality. From the JD-R model, a number of hypotheses on the mediation model were derived. They were based on two processes posited by the model: an energy-draining process in which work stressors along with a mediating effect of causal attribution for failures deplete the nurses’ energy, leading to turnover intention, and a motivational process in which personal resources along with a mediating effect of causal attribution for successes foster the nurses’ engagement in their work, leading to higher organizational identification and to decreased intention to leave the nursing job. For instance, it was expected that the relationship between work stressors and turnover intention could be explained (mediated) by a tendency to attribute one’s work failures to stable causes. The data were collected from among Finnish hospital nurses using e-questionnaires. Overall 934 nurses responded the questionnaires. Work stressors and personal resources were measured by five scales derived from the Occupational Stress Inventory-Revised (Osipow, 1998). Causal attribution was measured using the Occupational Attributional Style Questionnaire (Furnham, 2004). Work engagement was assessed through the Utrecht Work Engagement Scale (Schaufeli & al., 2002), turnover intention by the Van Veldhoven & Meijman (1994) scale, and organizational identification by the Mael & Ashforth (1992) measure. The results provided support for the function of causal attribution in the overall work stress process. Findings related to the moderation model can be divided into three main findings. First, external locus of causality along with job level moderated the relationship between work overload and cognitive coping. Hence, this interaction was evidenced only among nurses in non-supervisory positions. Second, external locus of causality and job level together moderated the relationship between physical environment and turnover intention. An opposite pattern of interaction was found for this interaction: among nurses, externality exacerbated the effect of perceived unpleasantness of the physical environment on turnover intention, whereas among supervisors internality produced the same effect. Third, job level also disclosed a moderation effect for controllability attribution over the relationship between physical environment and cognitive coping. Findings related to the mediation model for the energetic process indicated that the partial model in which work stressors have also a direct effect on turnover intention fitted the data better. In the mediation model for the motivational process, an intermediate mediation effect in which the effects of personal resources on turnover intention went through two mediators (e.g., causal dimensions and organizational identification) fitted the data better. All dimensions of causal attribution appeared to follow a somewhat unique pattern of mediation effect not only for energetic but also for motivational processes. Overall findings on mediation models partly supported the two simultaneous underlying processes proposed by the JD-R model. While in the energetic process the dimension of externality mediated the relationship between stressors and turnover partially, all the dimensions of causal attribution appeared to entail significant mediator effects in the motivational process. The general findings supported the moderation effect and the mediation effect of causal attribution in the work stress process. The study contributes to several research traditions, including the interaction approach, the JD-C, and the JD-R models. However, many potential functions of organizational causal attribution are yet to be evaluated by relevant academic and organizational research. Keywords: organizational causal attribution, optimistic / pessimistic attributional style, work stressors, organisational stress process, stressors in nursing profession, hospital nursing, JD-R model, personal resources, turnover intention, work engagement, organizational identification.

Distribución y variabilidad de Ralstonia solanacearum e.f. Smith agente causal de marchitez bacteriana en el cultivo de papa (Solanum tuberosum L), en tres departamentos del norte de Nicaragua (Esteli, Matagalpa y Jinotega)

La marchitez bacteriana de la papa causada por Ralstonia solanacearum (E. F. Smith) es una de las principales limitantes en la producción de es te cultivo. R.olanacearum es una especie altamente variable, el estudio de su diversidad poblacional es un importante factor a considerar para su control. Con el objetivo de conocer la distribución y la variabilidad, se realizó un estudio durante el período comprendido de Septiembre de 2006 a Enero de 2007, en diferentes localidades distribuidas en tres departamentos de Nicaragua (Estelí, Matagalpa y Jinotega ), donde se recolectaron 18 muestras de tejidos vegetales (tubérculos y tallos) de papa (Solanum tuberosum L.) y suelo, las que fueron analizadas en laboratorio de Microbiología de la Universidad Nacional Agraria (UNA), para el aislamiento, identificación y multiplicación de la bacteria. Se realizaron siembras en plato petri que contenían medio de cultivo medio agar sacarosa-peptona. Posterior a su aislamiento se realizó purificación en un medio específico (tetrazolium). Las cepas bacterianas se identificaron mediante la determinación de características culturales, morfológicas, fisiológicas y bioquímicas. En el primer caso, se observaron características de borde, elevación, consistencia y color de las cepas individuales cultivadas en el medio agar sacarosa- peptona. Las características morfológicas se comprobaron a través observación en el microscopio óptico. La confirmación de las características fisiológicas y bioquímicas, se realizó a través de pruebas de KOH al 3%, oxidasa, catalasa y revelación de flagelos. Las colonias bacterianas identificadas como Ralstonia solanacearun, se les realizó la prueba de carbohidratos para la caracterización de biovares, basada en la utilización de azúcares y oxidación de alcoholes (Hayward, 1991). Las pruebas de hipersensibilidad se realizaron en plantas de tabaco (Nicotiana tabacumL.). Estas fueron inoculadas mediante la infiltración de la suspensión bacteriana de 24 hrs de crecimiento. Como resultado de la prueba, se identificaron dieciséis aislamientos pertenecientes al biovar 3 y dos aislamientos pertenecientes al biovar 1. Siendo el biovar 3 el más prevaleciente en los sitios de muestro. La raza fue identificada en base a sintomatología presentada, resultando ser la raza 1.

Implementación del Modelo de Red Causal en un texto narrativo en español

Resumen: El Modelo de Red Causal propone que la estructura causal de una historia y su representación en la memoria episódica se asemejan a una red, en la que los acontecimientos resultan de una combinación de antecedentes causales, que a su vez tienen múltiples consecuencias. El estudio de la comprensión de textos según este modelo ha tendido a llevarse a cabo utilizando textos experimentales en inglés. En razón de ello, el objetivo de este trabajo consistió en presentar la aplicación del Modelo de Red Causal a un texto narrativo natural en español, a fin de abogar por su utilidad para examinar los procesos cognitivos involucrados en la comprensión textual.

Control químico de cercospora coffeicola (Berk y cooke) agente causal de la mancha de hierro en viveros de café (Coffea arabica L.)

La presente investigación fue realizada en la estación experimental del café de Masatepe; Nicaragua, durante los meses de Julio a Enero de los años 1975 a 1976. Consistió en la evaluación de doce Fungicidas para controlar Cercospera Coffeicole, agente causal de la “Mancha de Hierro “ en viveros de café. Como fuente de inòculo se utilizó plántulas infestadas por C. coffeicola; alrededor del ensayo. Para evaluar los daños ocasionados por este patógeno así como la fitotoxicidad de los productos sobre las plántulas, se utilizó el índice de infección propuesto por Grangier (24) y se elaboró una escala de fitotoxicidad, efectuándose un análisis de varianza en forma de parcelas divididas para determinar los efectos de los fungicidas y la fecha de mayor incidencia con los índices de o infección obtenidos. Basados en el índice de infección, Vigor de las plántulas y grado de fitotoxicidad los mejores fungicidas fueron: Difectatàn, MK-23, Kocide “101, Dithare M45 y cupravit ob-21 en dosis de 4, 4. 3.5.5 y 5 gramos de material comercial por litro de agua respectivamente. La mayor incidencia de C. Coffeicola se produjo durante el mes de Septiembre, elaborando su cuadro epidemiológico con el conjunto de datos climáticos

Monte Carlo-Based Bayesian group object tracking and causal reasoning

We present algorithms for tracking and reasoning of local traits in the subsystem level based on the observed emergent behavior of multiple coordinated groups in potentially cluttered environments. Our proposed Bayesian inference schemes, which are primarily based on (Markov chain) Monte Carlo sequential methods, include: 1) an evolving network-based multiple object tracking algorithm that is capable of categorizing objects into groups, 2) a multiple cluster tracking algorithm for dealing with prohibitively large number of objects, and 3) a causality inference framework for identifying dominant agents based exclusively on their observed trajectories.We use these as building blocks for developing a unified tracking and behavioral reasoning paradigm. Both synthetic and realistic examples are provided for demonstrating the derived concepts. © 2013 Springer-Verlag Berlin Heidelberg.

Spectral properties of a double-quantum-dot structure: A causal Green's function approach

Spectral properties of a double quantum dot (QD) structure are studied by a causal Green's function (GF) approach. The double QD system is modeled by an Anderson-type Hamiltonian in which both the intra- and interdot Coulomb interactions are taken into account. The GF's are derived by an equation-of-motion method and the real-space renormalization-group technique. The numerical results show that the average occupation number of electrons in the QD exhibits staircase features and the local density of states depends appreciably on the electron occupation of the dot.

Motivated Action Theory: A Formal Theory of Causal Reasoning

When we reason about change over time, causation provides an implicit preference: we prefer sequences of situations in which one situation leads causally to the next, rather than sequences in which one situation follows another at random and without causal connections. In this paper, we explore the problem of temporal reasoning --- reasoning about change over time --- and the crucial role that causation plays in our intuitions. We examine previous approaches to temporal reasoning, and their shortcomings, in light of this analysis. We propose a new system for causal reasoning, motivated action theory, which builds upon causation as a crucial preference creterion. Motivated action theory solves the traditional problems of both forward and backward reasoning, and additionally provides a basis for a new theory of explanation.

Learning World Models in Environments with Manifest Causal Structure

This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.

Combining Associational and Causal Reasoning to Solve Interpretation and Planning Problems

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.