Search results

The Voice of the Clinician project commenced during an era when practitioner burnout, dissatisfaction, and turnover became an increasingly global health workforce concern. One key problem is clinical staff not being empowered to voice their concerns to decision-makers, as was found in this case study of an Australian public health organization. The following research question informed the present study: What is a better committee system for clinician engagement in decision-making processes? The paper aims to discuss this issue.

The Mid North Coast Local Health District in New South Wales aspired to improve engagement between frontline clinicians and decision-makers. Social network analysis methods and mathematical modeling were used in the discovery of how committees are connected to each other and subsequently to other committee members.

This effort uncovered a hidden organizational architecture of 323 committees of 926 members which overall cost 84,729 person hours and AUD$2.923 million per annum. Furthermore, frontline clinicians were located far from centers of influence, just 37 percent of committees had terms of reference, and clinicians reported that meeting agendas were not being met.

In response to the findings, a technological platform was created so that the board of directors could visually see all the committees and the connections between them, thus creating ways to further improve communication, transparency of process, and – ultimately – clinician engagement.

The breakthrough idea is that all organizational meetings can be seen as a system of engagement and should be analyzed to determine and describe the points and pathways where clinician voice is blocked.

The topic of whistleblowing is achieving prominence as a question of social policy. Some influential voices are suggesting that far from whistleblowing — informing on organisations —, being socially undesirable, it may in certain circumstances be an activity deserving high praise. Inevitably it entails huge risks to the activist, and these risks need to be personally and carefully considered. John Banham, Director General of the Confederation of British Industry, wrote in support of the Social Audit report on the subject (Winfield 1990), and a committee established by the Speaker of the House of Commons has suggested the possibility of honouring whistleblowers in the British Honours system for their good corporate citizenship. There have also been landmark reports in America, Australia and Canada (Leahy 1978, Electoral and Administrative Review Commission 1990, Ontario Law Reform Commission 1986).

An historical saunter is taken through the development of approaches, legal and organisational, to deal with whistleblowing. Whistleblowing engenders strong emotions, and it is not surprising that some of the early pioneers met with stiff resistance. Some classic whistleblowing cases are analysed, notably Stanley Adams and charge nurse Graham Pink. Key developments and cases during the Thatcher period are outlined. Employment law was rather primitive, and failed to give much definitive protection to whistleblowers. The contribution of the Committee of Lord Nolan on Standards in Public Life is outlined. The area of higher education, and the pivotal territory of financial services, accounting and auditing also are treated.

Using an online survey design and a primary data collection of police officers working in a large city in the southern United States, the current study finds evidence that officers perceptions of police legitimacy can be divided into two types: self-identification and perceived external legitimacy. The study investigates the role of perceived organizational support, leader–member exchange and demographic factors in predicting perceptions of self-identification and perceived external legitimacy.

The police legitimacy literature has focused primarily on the public's perceptions of the legitimacy of police. There is limited understanding of the components of officers' attitudes towards police legitimacy, or the predictors of those components.

Results of the Ordinary Least Squares regression models indicate perceived organizational support mediates the relationship between leader–member exchange and self-identification and perceived external legitimacy. Exploratory mediation analyses indicate perceived organizational support mediates both of those relationships.

The results provide further evidence that the two types of self-perceived legitimacy are analytically distinct. They differ from previous work in that demographic and organizational variables predict each type similarly, and that one predictor (POS) mediates the influence of another (LMX). The results have implications for future police self-legitimacy research.

Looks at the 2000 Employment Research Unit Annual Conference held at the University of Cardiff in Wales on 6/7 September 2000. Spotlights the 76 or so presentations within and shows that these are in many, differing, areas across management research from: retail finance; precarious jobs and decisions; methodological lessons from feminism; call centre experience and disability discrimination. These and all points east and west are covered and laid out in a simple, abstract style, including, where applicable, references, endnotes and bibliography in an easy‐to‐follow manner. Summarizes each paper and also gives conclusions where needed, in a comfortable modern format.

Presents 35 abstracts from the 2001 Employment Research Unit Annual conference held at Cardiff Business School in September 2001. Attempts to explore the theme of changing politics of employment relations beyond and within the nation state, against a background of concern in the developed economies at the erosion of relatively advanced conditions of work and social welfare through increasing competition and international agitation for more effective global labour standards. Divides this concept into two areas, addressing the erosion of employment standards through processes of restructuring and examining attempts by governments, trade unions and agencies to re‐create effective systems of regulation. Gives case examples from areas such as India, Wales, London, Ireland, South Africa, Europe and Japan. Covers subjects such as the Disability Discrimination Act, minimum wage, training, contract workers and managing change.

This paper examines the use of “double breasting” as a means of union avoidance among multinational companies (MNCs). Double breasting refers to the practice whereby multi-establishment organizations simultaneously operate establishments on both union and non-union bases. Using survey data from the largest and most representative empirical investigation of employment practice in MNCs in Ireland, supplemented by qualitative data gathered from case-based investigations in the subsidiary operations of American-owned MNCs, we profile the incidence and pattern of this particular form of union avoidance as well as providing insights on management's rationale for so doing. Our findings suggest that a substantial and increasing number of unionized MNCs in Ireland are engaging in double breasting. This phenomenon is most evident among U.S. MNCs. We also find that employers, at both local and global levels, have proactively initiated double breasting as a strategic ploy to increase management prerogative and better position subsidiary operations to attract new investment from corporate levels.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.