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Institution

City University London

EducationLondon, United Kingdom
About: City University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Health care. The organization has 5735 authors who have published 17285 publications receiving 453290 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors present a framework that identifies four faces of power (i.e., coercion, manipulation, domination, and subjectification) and four sites of power, i.e. power enacted "in", "through", "over", and "against" organizations.
Abstract: This paper reviews and evaluates the concept of power in management and organization science. In order to organize the extant literature on this topic, we develop a framework that identifies four faces of power (i.e. coercion, manipulation, domination, and subjectification) and four sites of power (i.e. power enacted “in”, “through”, “over”, and “against” organizations). This allows us to evaluate assumptions both shared and contested in the field. Building on the review, the paper then points to potentially novel areas of research that may extend our understandings of organizational power in management and organization science.

255 citations

Journal ArticleDOI
TL;DR: In this article, a conceptual framework of value creation through video games is developed, highlighting important findings from existing research in marketing and other disciplines, and applying the framework to derive future research opportunities.

255 citations

Journal ArticleDOI
TL;DR: A systematic review and meta-analysis of published literature comparing women with polycystic ovary syndrome (PCOS) to control groups on anxiety and depression was conducted in this paper.
Abstract: BACKGROUND - Our aim was to assess differences in anxiety and depression between women with and without (controls) polycystic ovary syndrome (PCOS). METHODS - We conducted a systematic review and meta-analysis of published literature comparing women with PCOS to control groups on anxiety and depression. Electronic databases were searched up to 17 December 2010. The inverse variance method based, as appropriate, on a random- or fixed-effects model in Review Manager, Version 5 was used to analyse the data. RESULTS - Twelve comparative studies were included; all studies assessed depression (910 women with PCOS and 1347 controls) and six also assessed anxiety (208 women with PCOS and 169 controls). Analysis revealed higher depression (Z = 17.92, P < 0.00001; Hedges’ g = 0.82; 95% CI 0.73–0.92) and anxiety (Z = 5.03, P < 0.00001; Hedges’ g = 0.54; 95% CI 0.33–0.75) scores in the participants with, than without, PCOS. Studies controlling for BMI showed a smaller difference between women with PCOS and controls on anxiety and depression scores than studies not controlling for BMI. CONCLUSIONS - Women with PCOS on average tend to experience mildly elevated anxiety and depression, significantly more than women without PCOS. Women with PCOS with lower BMI tended to have slightly lower anxiety and depression scores, suggesting that having a lower BMI reduces anxiety and depression. Future studies might consider (i) controlling for BMI, (ii) stratifying by medication use in order to control for any anti-androgenic effects of medication and (iii) excluding women with polycystic ovaries from control groups.

254 citations

Journal ArticleDOI
TL;DR: In this article, a review of deep learning-based segmentation methods for cardiac image segmentation is provided, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound.
Abstract: Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

254 citations

Journal ArticleDOI
TL;DR: Assisted reproduction fathers were found to interact more with their child and to contribute more to parenting than fathers with a naturally conceived child and no group differences were found for either the presence of psychological disorder or children's perceptions of the quality of family relationships.
Abstract: relationships and the social and emotional development of children in families created as a result of the two most widely used reproductive technologies, in-vitro fertilization (IVY) and donor insemination (DI), in comparison with control groups of families with a naturally conceived child and adoptive families. Mothers of children conceived by assisted reproduction expressed greater warmth towards their child, were more emotionally involved with their child, interacted more with their child and reported less stress associated with parenting than mothers who conceived their child naturally. Similarly, assisted reproduction fathers were found to interact more with their child and to contribute more to parenting than fathers with a naturally conceived child. With respect to the children themselves, no group differences were found for either the presence of psychological disorder or for children's perceptions of the quality of family relationships. The findings relating to the quality of parenting and the socioemotional development of the children were similar in each of the four countries studied.

254 citations


Authors

Showing all 5822 results

NameH-indexPapersCitations
Andrew M. Jones10376437253
F. Rauscher10060536066
Thorsten Beck9937362708
Richard J. K. Taylor91154343893
Christopher N. Bowman9063938457
G. David Batty8845123826
Xin Zhang87171440102
Richard J. Cook8457128943
Hugh Willmott8231026758
Scott Reeves8244127470
Sarah-Jayne Blakemore8121129660
Mats Alvesson7826738248
W. John Edmunds7525224018
Sheng Chen7168827847
Christopher J. Taylor7141530948
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
2022188
20211,030
20201,011
2019939
2018879