How was patient empowerment portrayed in information leaflets describing the community pharmacy Medicines Use Review service in the UK?

Objective The Medicines Use Review (MUR) community pharmacy service was introduced in 2005 to enhance patient empowerment but the service has not been taken up as widely as expected. We investigated the depiction of the patient–pharmacist power relationship within MUR patient information leaflets. Methods We identified 11 MUR leaflets including the official Department of Health MUR booklet and through discourse analysis examined the way language and imagery had been used to symbolise and give meaning to the MUR service, especially the portrayal of the patient–pharmacist interactions and the implied power relations. Results A variety of terminology was used to describe the MUR, a service that aimed ultimately to produce more informed patients through the information imparted by knowledgeable, skilled pharmacists. Conclusion The educational role of the MUR overshadowed the intended patient empowerment that would take place with a true concordance-centred approach. Although patient empowerment was implied, this was within the boundaries of the biomedical model with the pharmacist as the expert provider of medicines information. Practice implications If patient empowerment is to be conveyed this needs to be communicated to patients through consistent use of language and imagery that portrays the inclusivity intended.

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.

Expert system to calculate the cofficient of friction : an approach to enhance traffic safety

Friction plays a key role in causing slipperiness as a low coefficient of friction on the road may result in slippery and hazardous conditions. Analyzing the strong relation between friction and accident risk on winter roads is a difficult task. Many weather forecasting organizations use a variety of standard and bespoke methods to predict the coefficient of friction on roads. This article proposes an approach to predict the extent of slipperiness by building and testing an expert system. It estimates the coefficient of friction on winter roads in the province of Dalarna, Sweden using the prevailing weather conditions as a basis. Weather data from the road weather information system, Sweden (RWIS) was used. The focus of the project was to use the expert system as a part of a major project in VITSA, within the domain of intelligent transport systems

Modelling an expert GIS system based on knowledge to evaluate oil spill environmental sensitivity

Oil spills cause great damage to coastal habitats, especially when rapid and suitable response measures are not taken. Establishing high priority areas is fundamental for the operation of response teams. Under this context and considering the need for keeping all geographical information up-to-date for emergencial use, the present study proposes employing a decision tree coupled with a knowledge-based approach using GIS to assign oil sensitivity indices to Brazilian coastal habitats. The modelled system works based on rules set by the official standards of Brazilian Federal Environment Organ. We tested it on one of the littoral regions of Brazil where transportation of petroleum is most intense: the coast of the municipalities of Sao Sebastiao and Caraguatatuba in the northern littoral of São Paulo state, Brazil. The system automatically ranked the littoral sensitivity index of the study area habitats according to geographical conditions during summer and winter; since index ranks of some habitats varied between these seasons because of sediment alterations. The obtained results illustrate the great potential of the proposed system in generating ESI maps and in aiding response teams during emergency operations. (C) 2009 Elsevier Ltd. All rights reserved.

A program for terpenoid skeleton prediction based on botanical information

This paper describes a new program developed to the SISTEMAT expert system, the SISOCBOT program. This program employs the botanical data analysis and predicts, at the end of analysis, the probable skeleton of a compound based on the input of family or genus names. The SISOCBOT program was tested with 78 samples involving 302 substances, pertaining to 38 carbon skeletons, and showed a high hit index on skeleton prediction, thus emphasizing the potential importance of these data for structural determination of natural products. © 2002 Elsevier Science Ltd. All rights reserved.

SISTAX - An intelligent tool for recovering information on natural products chemistry

This work describes the development of a new program, named SISTAX, for the expert system SISTEMAT. This program allows anyone interested in chemotaxonomy to carry out an intelligent search for organic compounds in databases through chemical structures. When coupled with can efficient encoding system, the program recognizes skeletal types and can find any substructural constraints demanded by the user. An example of an application of the program to the diterpene class found in plants is described.

Hispanic poverty from the current population survey and poverty of other self-identified ethnic groups from census information

Includes bibliography

Information and communication technology for disaster risk management in the Caribbean: subregional solutions to the challenge of limited human resource capacity

This document was adapted from a paper originally presented to the 8th Annual Caribbean Conference of Comprehensive Disaster Management, held in Montego Bay, Jamaica in December, 2013. It summarizes several activities that ECLAC has undertaken to assess the current state of information and communications technology (ICT) in the field of disaster risk management (DRM) as practiced in the Caribbean. These activities included an in-depth study that encompassed a survey of disaster management organizations in the region, an Expert Group Meeting attended by the heads of several national disaster offices, and a training workshop for professionals working in DRM in the Caribbean. One of the notable conclusions of ECLAC’s investigation on this topic is that the lack of human capacity is the single largest constraint that is faced in the implementation of ICT projects for DRM in the Caribbean. In considering strategies to address the challenge of limited human capacity at a regional level, two separate issues are recognized – the need to increase the ICT capabilities of disaster management professionals, and the need to make ICT specialists available to disaster management organizations to advise and assist in the implementation of technology-focused projects. To that end, two models are proposed to engage with this issue at a regional level. The first entails the establishment of a network of ICT trainers in the Caribbean to help DRM staff develop a strategic understanding of how technology can be used to further their organizational goals. The second is the development of “Centres of Excellence” for ICT in the Caribbean, which would enable the deployment of specialized ICT expertise to national disaster management offices on a project-by-project basis.

Towards a precise test for malaria diagnosis in the Brazilian Amazon: comparison among field microscopy, a rapid diagnostic test, nested PCR, and a computational expert system based on artificial neural networks

Abstract Background Accurate malaria diagnosis is mandatory for the treatment and management of severe cases. Moreover, individuals with asymptomatic malaria are not usually screened by health care facilities, which further complicates disease control efforts. The present study compared the performances of a malaria rapid diagnosis test (RDT), the thick blood smear method and nested PCR for the diagnosis of symptomatic malaria in the Brazilian Amazon. In addition, an innovative computational approach was tested for the diagnosis of asymptomatic malaria. Methods The study was divided in two parts. For the first part, passive case detection was performed in 311 individuals with malaria-related symptoms from a recently urbanized community in the Brazilian Amazon. A cross-sectional investigation compared the diagnostic performance of the RDT Optimal-IT, nested PCR and light microscopy. The second part of the study involved active case detection of asymptomatic malaria in 380 individuals from riverine communities in Rondônia, Brazil. The performances of microscopy, nested PCR and an expert computational system based on artificial neural networks (MalDANN) using epidemiological data were compared. Results Nested PCR was shown to be the gold standard for diagnosis of both symptomatic and asymptomatic malaria because it detected the major number of cases and presented the maximum specificity. Surprisingly, the RDT was superior to microscopy in the diagnosis of cases with low parasitaemia. Nevertheless, RDT could not discriminate the Plasmodium species in 12 cases of mixed infections (Plasmodium vivax + Plasmodium falciparum). Moreover, the microscopy presented low performance in the detection of asymptomatic cases (61.25% of correct diagnoses). The MalDANN system using epidemiological data was worse that the light microscopy (56% of correct diagnoses). However, when information regarding plasma levels of interleukin-10 and interferon-gamma were inputted, the MalDANN performance sensibly increased (80% correct diagnoses). Conclusions An RDT for malaria diagnosis may find a promising use in the Brazilian Amazon integrating a rational diagnostic approach. Despite the low performance of the MalDANN test using solely epidemiological data, an approach based on neural networks may be feasible in cases where simpler methods for discriminating individuals below and above threshold cytokine levels are available.

Advances in Teaching and Learning 2005 Webcast - Introductions and Expert Panel

Expert Panel: Documenting Teaching Scholarship for Promotion and Tenure Lemuel Moye, School of Public Health Miguel daCunha, School of Nursing William Tate, Dental School Katherine Loveland, Medical School