Isotopic Assessment of Sources of Surface Water Nitrate within the Oldman River Basin, Southern Alberta, Canada

Concentrations and isotopic compositions of NO-3 from the Oldman River (OMR) and some of its tributaries (Alberta, Canada) have been determined on a monthly basis since December 2000 to assess temporal and spatial variations of riverine NO-3 sources within the OMR basin. For the OMR sites, NO-3 -N concentrations reached up to 0.34 mg L-1, d15N-NO-3 values varied between –0.3 and +13.8‰, and d18O-NO-3 values ranged from –10.0 to +5.7‰. For the tributary sites, NO-3 -N concentrations were as high as 8.81 mg L-1, d15N-NO-3 values varied between –2.5 and +23.4‰, and d18O-NO-3 values ranged from –15.2 to +3.4‰. Tributaries in the western, relatively pristine forested part of the watershed add predominantly NO-3 to the OMR with d15N-NO-3 indicative of soil nitrification. In contrast, tributaries in the eastern agriculturally-urban-industrially-used part of the basin contribute NO-3 with d15N-NO-3 values of about +16‰ indicative of manure and/or sewage derived NO-3. This difference in d15N-NO-3 values of tributaries was found to be independent of the season, but rather indicates a spatial change in the NO-3 source, which correlates with land use changes within the OMR basin. As a consequence of tributary influx, d15N-NO-3 values in the Oldman River increased from +6‰ in the downstream direction (W to E), although [NO-3 -N] increased only moderately (generally

Index of sources of stress in nursing students: a confirmatory factor analysis.

Aim. This paper is a report of a study to test the proposed factor structure of the Index of Sources of Stress in Nursing Students. Background. Research across many countries has identified a number of sources of distress in nursing students but little attempt has been made to understand and measure sources of eustress or those stressors likely to enhance performance and well-being. The Index of Sources of Stress in Nursing Students was developed to do this. Exploratory factor analysis suggested a three-factor structure, the factors being labelled: learning and teaching; placement-related and course organization. It is important, however, to subject the instrument to confirmatory factor analysis as a further test of construct validity. Method. A convenience sample of final year nursing students (n = 176) was surveyed in one university in Northern Ireland in 2007. The Index of Sources of Stress in Nursing Students, which measures sources of stress likely to contribute to distress and eustress, was completed electronically. The LISREL programme was used to carry out the confirmatory factor analysis and test the factor structure suggested in the exploratory analysis. Findings. The proposed factor structure for the items measuring ‘Uplifts’ proved to be a good fit to the data and the proposed factor structure for the items measuring ‘Hassles’ showed adequate fit. Conclusion. In nursing programmes adopting the academic model and combining university-based learning with placement experience, this instrument can be used to help identify the sources of stress or course demands that students rate as distressing and those that help them to achieve. The validity of the ISSN could be further evaluated in other education settings.

Surveying nursing students on their sources of stress: A validation study

This study tested the psychometric properties of a questionnaire that measured sources of distress and eustress, or good stress, in nursing students. The Transactional model of stress construes stress in these different ways and is frequently used to understand sources of stress, coping and stress responses. Limited research has attempted to measure sources of distress and eustress or sources that can potentially enhance performance and well-being. A volunteer sample of final year nursing students (n = 120) was surveyed in the United Kingdom in 2007. The questionnaire measured sources of stress and measures of psychological well-being were taken to test construct validity. This was tested through an exploratory factor analysis. This reduced the questionnaire from 49 to 29 items and suggested three factors: learning and teaching, placement related and course organization; second, it was analysed by testing the assumptions of the Transactional model, the model on which the questionnaire was based. In line with the assumptions of the model, measures of distress related to adverse well-being, and measures of eustress related to healthier well-being responses. The test–retest reliability estimate was 0.8. While certain programme issues were associated with distress, placement-related experiences were the most important source of eustress.

Sources of Software Requirements Change from the Perspectives of Development and Maintenance

Changes to software requirements occur during initial development and subsequent to delivery, posing a risk to cost and quality while at the same time providing an opportunity to add value. Provision of a generic change source taxonomy will support requirements change risk visibility, and also facilitate richer recording of both pre- and post-delivery change data. In this paper we present a collaborative study to investigate and classify sources of requirements change, drawing comparison between those pertaining to software development and maintenance. We begin by combining evolution, maintenance and software lifecycle research to derive a definition of software maintenance, which provides the foundation for empirical context and comparison. Previously published change ‘causes’ pertaining to development are elicited from the literature, consolidated using expert knowledge and classified using card sorting. A second study incorporating causes of requirements change during software maintenance results in a taxonomy which accounts for the entire evolutionary progress of applications software. We conclude that the distinction between the terms maintenance and development is imprecise, and that changes to requirements in both scenarios arise due to a combination of factors contributing to requirements uncertainty and events that trigger change. The change trigger taxonomy constructs were initially validated using a small set of requirements change data, and deemed sufficient and practical as a means to collect common requirements change statistics across multiple projects.