Mental health nursing standards and practice indicators for Australia: a review of current literature

There are many changes and challenges facing the mental health care professional working in Australia in the 21st Century. Given the significance of their number and the considerable extent to which care is delivered by them, mental health nurses in particular must be at the forefront of the movement to enhance and improve mental health care. Mental health nurses in Australia must not only keep up with the changes, we should be setting the pace for others across the profession worldwide. The increasingly complex field of mental health nursing demands nurses who are not only equipped to face the challenges but are confident in doing so. Definitive guidelines for practice, clear expectations regarding outcomes and specific means by which to evaluate both practice and outcomes are vital. Strengthening the role and vision of mental health nursing so that there is clarity about both and highlighting core values by which to perform will enable us to become focused on our future and what we can expect to both give to and receive from our chosen profession and how we can, and do, contribute to mental health care. The role of the mental health nurse is undergoing expansion and there are new hurdles to overcome along with the new benefits this brings. To support this, nationally adopted, formalised standards of practice and means by which to measure these, i.e., practice indicators formerly known as clinical indicators, are required. It is important to have national standards and practice indicators because of the variances in the provision of mental health across Australia – different legislation regarding mental health policies and processes, different nursing registration bodies and Nursing Councils, for example – which create additional barriers to cohesion and uniformity. Improvements in the practice of mental health nursing lead to benefits for consumer outcomes as well as the overall quality of mental health care available in Australia. The emphasis on rights-based care, particularly consumer and carer rights, demands evidence-based, up-to-date mental health care delivered by competent, capable professionals. Documented expectations for performance by nurses will provide all involved with yardsticks by which to evaluate outcomes. Flowing on from these benefits are advances in mental health care generally and enhancements to Australia’s reputation and position within the health care arena throughout the world. Currently, the ‘Standards for Practice’ published by the Australian New Zealand College of Mental Health Nurses (ANZCMHN) in 1995 and the practice indicators developed by Skews et al. (2000) provide a less formal guide for mental health nurses working in Australia. While these earlier standards and practice indicators have played some role in supporting mental health nurses they have not been nationally or enthusiastically adopted and there are a multitude of reasons for this. This report reviews the current literature available on practice indicators and standards for practice and describes an evidence-based rationale as to why a review and renewal of these is required and why it is important, not just for mental health nurses but to the field of mental health in general. The term ‘practice indicator’ is used, except where a quotation utilises ‘clinical indicator’, to more accurately reflect the broad spectrum of nursing roles, i.e. not all mental health nursing work involves a clinical role.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Effect of Mn deficiency and legume inoculation on rhizosphere pH in highly alkaline soils

Although plant growth is often limited at high pH, little is known about root-induced changes in the rhizospheres of plants growing in alkaline soils. The effect of Mn deficiency in Rhodes grass (Chloris gayana cv. Pioneer) and of legume inoculation in lucerne (Medicago sativa L. cv. Hunter River), on the rhizosphere pH of plants grown in highly alkaline bauxite residue was investigated. Rhizosphere pH was measured quantitatively, with a micro pH electrode, and qualitatively, with an agar/pH indicator solution. Manganese deficiency in Rhodes grass increased root-induced acidification of the rhizosphere in a soil profile in which N was supplied entirely as NO3-. Rhizosphere pH in the Mn deficient plants was up to 1.22 pH units lower than that of the bulk soil, while only 0.90 to 0.62 pH units lower in plants supplied with adequate Mn. When soil N was supplied entirely as NO3-, rhizosphere acidification was more efficient in inoculated lucerne (1.75 pH unit decrease) than in non-inoculated lucerne (1.16 pH unit decrease). This difference in capacity to lower rhizosphere pH is attributable to the ability of the inoculated lucerne to fix atmospheric N2 rather than relying on the soil N (NO3 ) reserves as the non-inoculated plants. Rhizosphere acidification in both Rhodes grass and lucerne was greatest in the meristematic root zone and least in the maturation root zone.

An initial assessment of policies for saving a rare Australian glider: Experimental results, economics and ecology

Reviews the ecological status of the mahogany glider and describes its distribution, habitat and abundance, life history and threats to it. Three serial surveys of Brisbane residents provide data on the knowledge of respondents about the mahogany glider. The results provide information about the attitudes of respondents to the mahogany glider, to its conservation and relevant public policies and about variations in these factors as the knowledge of participants of the mahogany glider alters. Similarly data is provided and analysed about the willingness to pay of respondents to conserve the mahogany glider. Population viability analysis is applied to estimate the required habitat area for a minimum viable population of the mahogany glider to ensure at least a 95% probability of its survival for 100 years. Places are identified in Queensland where the requisite minimum area of critical habitat can be conserved. Using the survey results as a basis, the likely willingness of groups of Australians to pay for the conservation of the mahogany glider is estimated and consequently their willingness to pay for the minimum required area of its habitat. Methods for estimating the cost of protecting this habitat are outlined. Australia-wide benefits seem to exceed the costs. Establishing a national park containing the minimum viable population of the mahogany glider is an appealing management option. This would also be beneficial in conserving other endangered wildlife species. Therefore, additional economic benefits to those estimated on account of the mahogany glider itself can be obtained.

Polytomous item response theory models

Polytomous Item Response Theory Models provides a unified, comprehensive introduction to the range of polytomous models available within item response theory (IRT). It begins by outlining the primary structural distinction between the two major types of polytomous IRT models. This focuses on the two types of response probability that are unique to polytomous models and their associated response functions, which are modeled differently by the different types of IRT model. It describes, both conceptually and mathematically, the major specific polytomous models, including the Nominal Response Model, the Partial Credit Model, the Rating Scale model, and the Graded Response Model. Important variations, such as the Generalized Partial Credit Model are also described as are less common variations, such as the Rating Scale version of the Graded Response Model. Relationships among the models are also investigated and the operation of measurement information is described for each major model. Practical examples of major models using real data are provided, as is a chapter on choosing an appropriate model. Figures are used throughout to illustrate important elements as they are described.

Long-term potentiation of synaptic transmission in the avian hippocampus

The avian hippocampus plays a pivotal role in memory required for spatial navigation and food storing. Here we have examined synaptic transmission and plasticity within the hippocampal formation of the domestic chicken using an in vitro slice preparation. With the use of sharp microelectrodes we have shown that excitatory synaptic inputs in this structure are glutamatergic and activate both NMDA-and AMPA-type receptors on the postsynaptic membrane. In response to tetanic stimulation, the EPSP displayed a robust long-term potentiation (LTP) lasting >1 hr. This LTP was unaffected by blockade of NMDA receptors or chelation of postsynaptic calcium. Application of forskolin increased the EPSP and reduced paired-pulse facilitation: (PPF), indicating an increase in release probability. In contrast, LTP was not associated with a change in the PPF ratio. Induction of LTP did not occlude the effects of forskolin. Thus, in contrast to NMDA receptor-independent LTP in the mammalian brain, LTP in the chicken hippocampus is not attributable to a change in the probability of transmitter release and does not require activation of adenylyl cyclase, These findings indicate that a novel form of synaptic plasticity might underlie learning in the avian hippocampus.

Metabolite mean transit times in the liver as predicted by various models of hepatic elimination

Predicted area under curve (AUC), mean transit time (MTT) and normalized variance (CV2) data have been compared for parent compound and generated metabolite following an impulse input into the liver, Models studied were the well-stirred (tank) model, tube model, a distributed tube model, dispersion model (Danckwerts and mixed boundary conditions) and tanks-in-series model. It is well known that discrimination between models for a parent solute is greatest when the parent solute is highly extracted by the liver. With the metabolite, greatest model differences for MTT and CV2 occur when parent solute is poorly extracted. In all cases the predictions of the distributed tube, dispersion, and tasks-in-series models are between the predictions of the rank and tube models. The dispersion model with mixed boundary conditions yields identical predictions to those for the distributed tube model (assuming an inverse gaussian distribution of tube transit times). The dispersion model with Danckwerts boundary conditions and the tanks-in series models give similar predictions to the dispersion (mixed boundary conditions) and the distributed tube. The normalized variance for parent compound is dependent upon hepatocyte permeability only within a distinct range of permeability values. This range is similar for each model but the order of magnitude predicted for normalized variance is model dependent. Only for a one-compartment system is the MIT for generated metabolite equal to the sum of MTTs for the parent compound and preformed metabolite administered as parent.

A study of the stability of subcycling algorithms in structural dynamics

Algorithms for explicit integration of structural dynamics problems with multiple time steps (subcycling) are investigated. Only one such algorithm, due to Smolinski and Sleith has proved to be stable in a classical sense. A simplified version of this algorithm that retains its stability is presented. However, as with the original version, it can be shown to sacrifice accuracy to achieve stability. Another algorithm in use is shown to be only statistically stable, in that a probability of stability can be assigned if appropriate time step limits are observed. This probability improves rapidly with the number of degrees of freedom in a finite element model. The stability problems are shown to be a property of the central difference method itself, which is modified to give the subcycling algorithm. A related problem is shown to arise when a constraint equation in time is introduced into a time-continuous space-time finite element model. (C) 1998 Elsevier Science S.A.

Critical sets for families of Latin squares

In this paper I give details of new constructions for critical sets in latin squares. These latin squares, of order n, are such that they can be partitioned into four subsquares each of which is based on the addition table of the integers module n/2, an isotopism of this or a conjugate.

A mathematical model of HIV transmission in NSW prisons

A mathematical model was developed to estimate HIV incidence in NSW prisons. Data included: duration of imprisonment; number of inmates using each needle; lower and higher number of shared injections per IDU per week; proportion of IDUs using bleach; efficacy of bleach; HIV prevalence and probability of infection. HIV prevalence in IDUs in prison was estimated to have risen from 0.8 to 5.7% (12.2%) over 180 weeks when using lower (and higher) values for frequency of shared injections. The estimated minimum (and maximum) number of IDU inmates infected with HIV in NSW prisons was 38 (and 152) in 1993 according to the model. These figures require confirmation by seroincidence studies. (C) 1998 Published by Elsevier Science Ireland Ltd. All rights reserved.

Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network

Motivation: Prediction methods for identifying binding peptides could minimize the number of peptides required to be synthesized and assayed, and thereby facilitate the identification of potential T-cell epitopes. We developed a bioinformatic method for the prediction of peptide binding to MHC class II molecules. Results: Experimental binding data and expert knowledge of anchor positions and binding motifs were combined with an evolutionary algorithm (EA) and an artificial neural network (ANN): binding data extraction --> peptide alignment --> ANN training and classification. This method, termed PERUN, was implemented for the prediction of peptides that bind to HLA-DR4(B1*0401). The respective positive predictive values of PERUN predictions of high-, moderate-, low- and zero-affinity binder-a were assessed as 0.8, 0.7, 0.5 and 0.8 by cross-validation, and 1.0, 0.8, 0.3 and 0.7 by experimental binding. This illustrates the synergy between experimentation and computer modeling, and its application to the identification of potential immunotheraaeutic peptides.