Saint Mary's University

About: Saint Mary's University is a education organization based out in Halifax, Nova Scotia, Canada. It is known for research contribution in the topics: Population & Stars. The organization has 1931 authors who have published 4993 publications receiving 143226 citations.

Workplace empowerment, incivility, and burnout: impact on staff nurse recruitment and retention outcomes

Abstract: Aim The aim of this study was to examine the influence of empowering work conditions and workplace incivility on nurses’ experiences of burnout and important nurse retention factors identified in the literature. Background A major cause of turnover among nurses is related to unsatisfying workplaces. Recently, there have been numerous anecdotal reports of uncivil behaviour in health care settings. Method We examined the impact of workplace empowerment, supervisor and coworker incivility, and burnout on three employee retention outcomes: job satisfaction, organizational commitment, and turnover intentions in a sample of 612 Canadian staff nurses. Results Hierarchical multiple linear regression analyses revealed that empowerment, workplace incivility, and burnout explained significant variance in all three retention factors: job satisfaction (R2 = 0.46), organizational commitment (R2 = 0.29) and turnover intentions (R2 = 0.28). Empowerment, supervisor incivility, and cynicism most strongly predicted job dissatisfaction and low commitment (P < 0.001), whereas emotional exhaustion, cynicism, and supervisor incivility most strongly predicted turnover intentions. Conclusions In our study, nurses’ perceptions of empowerment, supervisor incivility, and cynicism were strongly related to job satisfaction, organizational commitment, and turnover intentions. Implications for nursing management Managerial strategies that empower nurses for professional practice may be helpful in preventing workplace incivility, and ultimately, burnout.

Broad line emission from iron K- and L-shell transitions in the active galaxy 1H 0707-495.

Abstract: The emission line arising from a transition of an electron from the iron K shell to the ground state (the K line) is prominent in the reflection spectrum of the hard X-ray continuum irradiating dense accreting matter around a black hole. The corresponding iron L-line emission should be detectable when iron abundance is high. That's the theory, and now broad iron L-line emission has been observed, together with the broad K line in the narrow-line Seyfert galaxy 1H0707. There is a reverberation lag of about 30 s between the direct X-ray continuum and its reflection from matter falling into the hole, a timescale comparable to the light-crossing time of the innermost radii around a supermassive black hole. This discovery opens a window on events close to the black hole event horizon in these objects. Emission arising from a transition of an electron from the iron K shell to the ground state (the K line) is prominent in the reflection spectrum created by the hard X-ray continuum irradiating the dense accreting matter around a black hole. Here the presence of both iron K and L emission is reported in the spectrum of the active galaxy 1H 0707-495. There is a 'reverberation lag' with a timescale comparable to the light-crossing time of the innermost radii around a supermassive black hole. Since the 1995 discovery of the broad iron K-line emission from the Seyfert galaxy MCG–6-30-15 (ref. 1), broad iron K lines have been found in emission from several other Seyfert galaxies2, from accreting stellar-mass black holes3 and even from accreting neutron stars4. The iron K line is prominent in the reflection spectrum5,6 created by the hard-X-ray continuum irradiating dense accreting matter. Relativistic distortion7 of the line makes it sensitive to the strong gravity and spin of the black hole8. The accompanying iron L-line emission should be detectable when the iron abundance is high. Here we report the presence of both iron K and iron L emission in the spectrum of the narrow-line Seyfert 1 galaxy9 1H 0707-495. The bright iron L emission has enabled us to detect a reverberation lag of about 30 s between the direct X-ray continuum and its reflection from matter falling into the black hole. The observed reverberation timescale is comparable to the light-crossing time of the innermost radii around a supermassive black hole. The combination of spectral and timing data on 1H 0707-495 provides strong evidence that we are witnessing emission from matter within a gravitational radius, or a fraction of a light minute, from the event horizon of a rapidly spinning, massive black hole.

Interval Set Clustering of Web Users with Rough K -Means

Abstract: Data collection and analysis in web mining faces certain unique challenges. Due to a variety of reasons inherent in web browsing and web logging, the likelihood of bad or incomplete data is higher than conventional applications. The analytical techniques in web mining need to accommodate such data. Fuzzy and rough sets provide the ability to deal with incomplete and approximate information. Fuzzy set theory has been shown to be useful in three important aspects of web and data mining, namely clustering, association, and sequential analysis. There is increasing interest in research on clustering based on rough set theory. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users. Researchers have pointed out some important differences between clustering in conventional applications and clustering in web mining. For example, the clusters and associations in web mining do not necessarily have crisp boundaries. As a result, researchers have studied the possibility of using fuzzy sets in web mining clustering applications. Recent attempts have used genetic algorithms based on rough set theory for clustering. However, the genetic algorithms based clustering may not be able to handle the large amount of data typical in a web mining application. This paper proposes a variation of the K-means clustering algorithm based on properties of rough sets. The proposed algorithm represents clusters as interval or rough sets. The paper also describes the design of an experiment including data collection and the clustering process. The experiment is used to create interval set representations of clusters of web visitors.