Sing & Grow : the co-existence of evaluation research and clinical practice in an early intervention music therapy project

Sing & Grow is a short term early intervention music therapy program for at risk families. Sing & Grow uses music to strengthen parent-child relationships by increasing positive parent-child interactions, assisting parents to bond with their children, and extending the repertoire of parents’ skills in relating to their child through interactive . Both the Australian and New Zealand governments are looking for evidence based research to highlight the effectiveness of funded programs in early childhood. As a government funded program, independent evaluation is a requirement of the delivery of the service. This paper explains the process involved in setting up and managing this large scale evaluation from engaging the evaluators and designing the project, to the data gathering stage. It describes the various challenges encountered and concludes that a highly collaborative and communicative partnership bet en researchers and clinicians is essential to ensure data can be gathered with minimal disturbance to clinical music therapy practice.

Clinical factors and technological barriers as determinants for the intention to use wireless handheld technology in healthcare environment: an Indian case study

Abstract]: Traditional technology adoption models identified ‘ease of use’ and ‘usefulness’ as the dominating factors for technology adoption. However, recent studies in healthcare have established that these two factors are not always reliable on their own and other factors may influence technology adoption. To establish the identity of these additional factors, a mixed method approach was used and data were collected through interviews and a survey. The survey instrument was specifically developed for this study so that it is relevant to the Indian healthcare setting. We identified clinical management and technological barriers as the dominant factors influencing the wireless handheld technology adoption in the Indian healthcare environment. The results of this study showed that new technology models will benefit by considering the clinical influences of wireless handheld technology, in addition to known factors. The scope of this study is restricted to wireless handheld devices such as PDAs, smart phones, and handheld PCs Gururajan, Raj and Hafeez-Baig, Abdul and Gururajan, Vijaya

Molecular classification of cutaneous malignant melanoma by gene expression profiling

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

On a Syntactic Characterization of Classification with a Mind Change Bound

Most learning paradigms impose a particular syntax on the class of concepts to be learned; the chosen syntax can dramatically affect whether the class is learnable or not. For classification paradigms, where the task is to determine whether the underlying world does or does not have a particular property, how that property is represented has no implication on the power of a classifier that just outputs 1’s or 0’s. But is it possible to give a canonical syntactic representation of the class of concepts that are classifiable according to the particular criteria of a given paradigm? We provide a positive answer to this question for classification in the limit paradigms in a logical setting, with ordinal mind change bounds as a measure of complexity. The syntactic characterization that emerges enables to derive that if a possibly noncomputable classifier can perform the task assigned to it by the paradigm, then a computable classifier can also perform the same task. The syntactic characterization is strongly related to the difference hierarchy over the class of open sets of some topological space; this space is naturally defined from the class of possible worlds and possible data of the learning paradigm.

Occupational Therapy and Clinical Research in Mental Health Rehabilitation

Practitioners working in Australian mental health services are faced with the challenge of providing appropriate evidence-based interventions that lead to measurable improvement and good outcomes. Current government policy is committed to the development of strategic mental health research. One focus has been on under-researched practice areas, which include the development of psychosocial rehabilitation systems and models that facilitate recovery. To meet this challenge, an Australian rehabilitation service formed a collaborative partnership with a university. The purposes of the collaboration were to implement new forms of service delivery based on consumer need and evidence and to design research projects to evaluate components of the rehabilitation programme. This article examines the process of developing the collaboration and provides examples of how research projects have been used to inform practice and improve the effectiveness of service delivery. Challenges to the sustainability of this kind of collaboration are considered.

Substance-induced psychosis : a diagnostic conundrum

Objective: To critically examine the DSM-IV-TR criteria for Substance-Induced Psychotic Disorder (SIPD). Data sources: Leading electronic databases (such as Medline, Pubmed) were searched for the years 1992 through 2007, using combinations of the following key search terms: substance abuse/dependence, alcohol, marijuana, cannabis, methamphetamine, crack, cocaine, amphetamine, ecstasy, ketamine, phencyclidine, LSD, mental health, drug-induced psychosis, substance-induced psychosis, psychosis, schizophrenia. References identified from bibliographies of pertinent articles and books in the field were also collected and reviewed. Data extraction: Only research studies or case reports series that presented data on populations diagnosed with SIPD using clinical or structured diagnostic interviews published in English were used to assess the validity of the current SIPD criteria. Data synthesis: We identified 49 articles that presented clinical data on SIPD. The majority of these publications were case reports, with only 18 articles specifically focusing on delineating the clinical characteristics or outcomes of individuals diagnosed with SIPD. While several large studies have recently been conducted to assess the stability of SIPD, there is a dearth of research rigorously examining the validity of DSM-IV diagnostic criteria across substances. Conclusions: There remains a striking paucity of information on the outcome, treatment and best practice for substance-associated psychotic episodes. Further work is clearly required before the advent of DSM-V. We propose an alternative, broader classification that better reflects the current evidence base, inferring association rather than causation.

Classification of packet contents for malware detection

Many existing schemes for malware detection are signature-based. Although they can effectively detect known malwares, they cannot detect variants of known malwares or new ones. Most network servers do not expect executable code in their in-bound network traffic, such as on-line shopping malls, Picasa, Youtube, Blogger, etc. Therefore, such network applications can be protected from malware infection by monitoring their ports to see if incoming packets contain any executable contents. This paper proposes a content-classification scheme that identifies executable content in incoming packets. The proposed scheme analyzes the packet payload in two steps. It first analyzes the packet payload to see if it contains multimedia-type data (such as . If not, then it classifies the payload either as text-type (such as or executable. Although in our experiments the proposed scheme shows a low rate of false negatives and positives (4.69% and 2.53%, respectively), the presence of inaccuracies still requires further inspection to efficiently detect the occurrence of malware. In this paper, we also propose simple statistical and combinatorial analysis to deal with false positives and negatives.

Simple classification of walking activities using commodity smart phones

People interact with mobile computing devices everywhere, while sitting, walking, running or even driving. Adapting the interface to suit these contexts is important, thus this paper proposes a simple human activity classification system. Our approach uses a vector magnitude recognition technique to detect and classify when a person is stationary (or not walking), casually walking, or jogging, without any prior training. The user study has confirmed the accuracy.