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Article
Publication date: 9 March 2012

Suzanne Cahill, Daphne Doran and Max Watson

This study aims to contribute to improving quality of life for people with end stage dementia living in residential care settings by investigating the experiences of elderly…

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Abstract

Purpose

This study aims to contribute to improving quality of life for people with end stage dementia living in residential care settings by investigating the experiences of elderly spouses whose relatives died with end‐stage dementia in nursing homes in both Northern Ireland (NI) and the Republic of Ireland (RoI). A second aim is to develop guidelines for nursing home staff for the delivery of quality care to residents with end stage dementia in residential institutions.

Design/methodology/approach

This study had two phases. Phase one involved conducting in‐depth qualitative interviews with spouse caregivers whose relatives had died from dementia in long stay care environments. Phase two entailed incorporating the information gathered through the in‐depth interviews into draft guidelines and disseminating these to a multi‐disciplinary group of health service professionals for their critical appraisal and ratification.

Findings

Findings showed that the (EoL) care delivered was deemed by most elderly spouses to be of high quality, with person centred, individual, kind, professional care highly valued. Areas of dissatisfaction noted included poor communication, lack of involvement in key decision making, and poor symptoms control.

Originality/value

Based on the study's findings, guidelines for the delivery of quality care in long stay residential institutions were developed in consultation with eight health service professionals. The authors hope these guidelines will contribute to improvements in the care of people with dementia at end of life and will form the basis for the future development of policy, practices and procedures.

Details

Quality in Ageing and Older Adults, vol. 13 no. 1
Type: Research Article
ISSN: 1471-7794

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Article
Publication date: 1 February 2016

Yuxian Eugene Liang and Soe-Tsyr Daphne Yuan

What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies…

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Abstract

Purpose

What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis.

Design/methodology/approach

This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network.

Findings

This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies.

Originality/value

The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.

Details

Internet Research, vol. 26 no. 1
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 1 April 1988

Paul Nieuwenhuysen

The following bibliography focuses mainly on programs which can run on IBM microcomputers and compatibles under the operating system PC DOS/MS DOS, and which can be used in online…

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Abstract

The following bibliography focuses mainly on programs which can run on IBM microcomputers and compatibles under the operating system PC DOS/MS DOS, and which can be used in online information and documentation work. They fall into the following categories:

Details

The Electronic Library, vol. 6 no. 4
Type: Research Article
ISSN: 0264-0473

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