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.

Design of speaker identification schemes for large number of speakers (A)

Design of speaker identification schemes for a small number of speakers (around 10) with a high degree of accuracy in controlled environment is a practical proposition today. When the number of speakers is large (say 50–100), many of these schemes cannot be directly extended, as both recognition error and computation time increase monotonically with population size. The feature selection problem is also complex for such schemes. Though there were earlier attempts to rank order features based on statistical distance measures, it has been observed only recently that the best two independent measurements are not the same as the combination in two's for pattern classification. We propose here a systematic approach to the problem using the decision tree or hierarchical classifier with the following objectives: (1) Design of optimal policy at each node of the tree given the tree structure i.e., the tree skeleton and the features to be used at each node. (2) Determination of the optimal feature measurement and decision policy given only the tree skeleton. Applicability of optimization procedures such as dynamic programming in the design of such trees is studied. The experimental results deal with the design of a 50 speaker identification scheme based on this approach.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.

Voice source characterization using pitch synchronous discrete cosine transform for speaker identification

A characterization of the voice source (VS) signal by the pitch synchronous (PS) discrete cosine transform (DCT) is proposed. With the integrated linear prediction residual (ILPR) as the VS estimate, the PS DCT of the ILPR is evaluated as a feature vector for speaker identification (SID). On TIMIT and YOHO databases, using a Gaussian mixture model (GMM)-based classifier, it performs on par with existing VS-based features. On the NIST 2003 database, fusion with a GMM-based classifier using MFCC features improves the identification accuracy by 12% in absolute terms, proving that the proposed characterization has good promise as a feature for SID studies. (C) 2015 Acoustical Society of America

Speaker verification based on the fusion of speech acoustics and inverted articulatory signals

We propose apractical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker verification, we find that concatenating articulatory features obtained from measured speech production data with conventional Mel-frequency cepstral coefficients (MFCCs) improves the performance dramatically. However, since directly measuring articulatory data is not feasible in many real world applications, we also experiment with estimated articulatory features obtained through acoustic-to-articulatory inversion. We explore both feature level and score level fusion methods and find that the overall system performance is significantly enhanced even with estimated articulatory features. Such a performance boost could be due to the inter-speaker variation information embedded in the estimated articulatory features. Since the dynamics of articulation contain important information, we included inverted articulatory trajectories in text dependent speaker verification. We demonstrate that the articulatory constraints introduced by inverted articulatory features help to reject wrong password trials and improve the performance after score level fusion. We evaluate the proposed methods on the X-ray Microbeam database and the RSR 2015 database, respectively, for the aforementioned two tasks. Experimental results show that we achieve more than 15% relative equal error rate reduction for both speaker verification tasks. (C) 2015 Elsevier Ltd. All rights reserved.