Search results

Libraries as they are known today can be defined by the term Library 1.0. This defines the way resources are kept on shelves or at a computer behind a login. These resources can be taken from a shelf, checked out to the librarian, taken home for a certain length of time and absorbed, and then taken back to the library for someone else to use. Library 1.0 is a one‐directional service that takes people to the information that they require. Library 2.0 – or L2 as it is now more commonly addressed as – aims to take the information to the people by bringing the library service to the internet and getting the users more involved by encouraging feedback participation. This paper seeks to present an overview of Library 2.0.

This paper presents an overview of Web 2.0 including definitions, technologies involved and sites currently advocated as examples of Web 2.0.

The major difference between Library 1.0 and L2 is that Library 1.0 only allows for a one‐way flow of information while L2 is a read‐write library that gives library users the power to decide the service that they get. L2 reinforces the role libraries play in the community by building on today's best and continually improving the service. L2 can be summarized as being user‐driven and aiming to save each library user time in retrieving information.

Libraries have been around for centuries and are considered places in which books, journals, CDs, etc. are kept for reference or for borrowing by the public. The term L2 was believed to have been first made by Michael Casey in his blog LibraryCrunch. Chad and Paul Miller describe Library 2.0 (L2) as a concept, very different from the service one knows today, that operates according to the expectations of today's users. They state that with this concept the library will make information available wherever and whenever the user requires it. One point to note here is that this concept is not about replacing the 1.0 technology already being used but rather about adding additional functionality.

This paper aims to explains how relatively simple nurse staffing formulas from “best practice” ward dependency‐acuity data can be used for nursing workforce planning and development.

The paper combines literature, detailed ward surveys, workshop and expert group/stakeholder information to generate and test care levels/nurse multipliers for setting ward establishments.

The paper finds that professional‐judgement based ward staffing can be abandoned, while complex acuity‐quality, timed‐task and regression‐based nurse staffing algorithms for setting ward establishments may be unnecessary since the new multipliers, underpinned by robust validity and reliability testing, seem to be remarkably accurate nurse‐staffing determiners at a fraction of the cost.

As care levels and multipliers stand they are suitable only for UK National Health Service acute wards. Primary care, mental health, learning disability and other specialist group care levels and multipliers need developing.

Users, at a minimum, can adopt care level data and multiplier staffing recommendations for benchmarking purposes. Ultimately, the algorithms can be used to: adjust ward establishments according to workload; or set staffing for new, inpatient services.

The paper offers a simple system for assessing patients' nursing needs and setting ward staffing accordingly.

The purpose of this paper is to examine the relationship of schedule flexibility with performance and satisfaction in the work and family domains, and whether these associations are mediated by the work‐family interface. Possible gender differences in the putative benefits of schedule flexibility are also to be explored.

Using a sample of 607 full‐time employees in either schedule flexibility or traditional working arrangements the authors tested a moderated‐mediation model. Regression was used to test the mediation of work‐family and the moderation of gender to the schedule flexibility to work‐family path.

Both work‐to‐family conflict and work‐to‐family enrichment are mediating mechanisms in the relationship of schedule flexibility with outcomes. More specifically, full mediation was found for job satisfaction and family performance for both enrichment and conflict while partial mediation was found for family satisfaction with enrichment only and mediation was not supported for job performance. Finally, gender moderated the schedule flexibility to work‐family conflict relationship such that women benefited more from flexible working arrangements than men.

The paper adds value by examining a mediation mechanism in the schedule flexibility with the outcome relationship of the work‐family interface. It also adds value by including work‐family enrichment which is a key variable but has little research. Finally, it adds value by demonstrating that schedule flexibility plays a stronger role for women than men regarding the work‐family interface.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively.

In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series.

In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models.

In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

Many individuals in drug treatment courts (DTCs) have co-occurring mental health and substance use disorders (COD), which can negatively impact treatment engagement, behavioral health and criminal justice outcomes. This paper aims to report results of DTC participants with a COD, who received a 12-month wraparound treatment intervention called MISSION-Criminal Justice (MISSION-CJ) alongside DTC to improve treatment engagement and behavioral health outcomes and reduce reincarcerations.

In this pre-post, single-group pilot, 48 clients enrolled and 81% completed 12-month follow-up assessments (N = 39) and weekly MISSION-CJ fidelity for type and intensity of services delivered. Generalized linear mixed models (GLMMs) were computed with a fixed term for fidelity (e.g. high or low MISSION-CJ), time and a fidelity x time interaction term.

Among participants, at 12 months, 81% of the participants remained engaged in treatment at study completion, and 89% had high MISSON-CJ fidelity. Clients demonstrated significant reductions from baseline to 12 months in average nights in jail (B = −0.1849511, p < 0.0344), mental health symptoms via the Behavior and Symptom Identification Scale (BASIS) total and subscale scores (B = −0.121613, p < 0.0186) and trauma symptoms on the PTSD Checklist-5 (PCL-5) (B = −0.928791, p < 0.0138). High MISSION-CJ fidelity further improved criminal justice, and behavioral health outcomes.

This was the first reported 12-month MISSION-CJ trial. While feasible to implement, given the design limitations, future research should include a large randomized controlled trial.

To understand ageing well, one needs to study not only those who are ageing but also the places within and with which people are ageing. In the past, much ageing‐well research has been focused on ensuring individuals have the “right” resources and are engaged in the “best” types of activities. However, recent theorizing has prompted the study of ageing well as a process of making sense of self amid later‐life changes. Building on Rowles' attachment‐to‐place work, the purpose of this paper is to consider how the “thick concreteness” of place influences later‐life meaning making.

This is a theoretical paper on ageing.

The paper draws on Casey's phenomenological conceptualization of places as imprinting themselves on bodies and selves, much as humans shape the places they inhabit. Data from interviews with older rural women in western Canada illustrates how this conceptualization can enhance understanding of ageing well relative to place as a physical, socio‐cultural and temporal phenomenon. In a place that has been depicted as inhospitable, participants have chosen to stay even as practically invisible kin and community “keepers” on the “frontier”.

This original paper suggests that to age well is to age locally and to make sense not only for self about self and one's own ageing but also for ageing in mutually compatible ways in that place.

Government cash forecasting is central to achieving effective government cash management but research in this area is scarce. The purpose of this paper is to address this shortcoming by developing a government cash forecasting model with an accuracy acceptable to the cash manager in emerging economies.

The paper follows “top-down” approach to develop a government cash forecasting model. It uses the Indonesian Government expenditure data from 2008 to 2015 as an illustration. The study utilises ARIMA, neural network and hybrid models to investigate the best procedure for predicting government expenditure.

The results show that the best method to build a government cash forecasting model is subject to forecasting performance measurement tool and the data used.

The study uses the data from one government only as its sample, which may limit the ability to generalise the results to a wider population.

This paper is novel in developing a government cash forecasting model in the context of emerging economies.

This article seeks to consider the lessons from one of the worst failures in adult protection in the UK in recent times: the abuse of a number of patients with learning disabilities or autism and challenging behaviour over a number of years at Winterbourne View private hospital in the outskirts of Bristol. The abuse persisted, irrespective of a number of attempts to alert a broad range of regulatory authorities and health professionals about the situation.

The article provides a detailed analysis of the lessons for professionals responsible for adult protection by one of the journalists most responsible for exposing the abuse at Winterbourne View private hospital. Drawing on information the BBC uncovered during the making of its two films about the subject, the author shares details of relevance to professionals responsible for adult protection and considers the implications of the catastrophic failure to protect vulnerable people.

This article shows how the lessons from the abuse at Winterbourne View have permeated only to some areas and professionals, not necessarily to where those lessons are most needed. The author suggests that further efforts are required to prevent another, similar scandal happening elsewhere in the UK.

The paper is a unique piece, sharing experiences from a journalist involved with exposing a scandal directly with professionals responsible for adult protection.

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.