Effects of patient-focused care on seclusion in a psychiatric intensive care unit

The objective of the study was to evaluate whether the introduction of patient-focused nursing care affected the number of seclusions and the length of time patients spent in seclusion, in an acute psychiatric unit. The study used a pre-intervention–post-intervention design and was conducted in an eight-bed locked unit within a large regional general hospital in Queensland, Australia. The medical records of all people who were secluded as part of their management while in hospital, during two 6-month periods, were retrospectively reviewed. Changes to the ways in which nurses conducted their daily activities were implemented during the time between the data collection periods. There were no differences between the groups with respect to the number of times a patient was secluded. However, following implementation of patient-focused care, there was a reduction in the length of time for which patients were secluded. The only change in medication administration was that post-implementation, Haloperidol was used in fewer seclusion episodes. The findings indicate that changes to nursing practice may result in closer monitoring of patients and a reduction in the time patients spend secluded in acute inpatient psychiatric settings.

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

Focused beam reflectance technique for in situ particle sizing in wastewater treatment settling tanks

Due to the complexities involved with measuring activated sludge floc size distributions, this parameter has largely been ignored by wastewater researchers and practitioners. One of the major reasons has been that instruments able to measure particle size distributions were complex, expensive and only provided off-line measurements. The Focused Beam Reflectance Method (FBRM) is one of the rare techniques able to measure the particle size distribution in situ. This paper introduces the technique for monitoring wastewater treatment systems and compares its performance with other sizing techniques. The issue of the optimal focal point is discussed, and similar conclusions as found in the literature for other particulate systems are drawn. The study also demonstrates the capabilities of the FBRM in evaluating the performance of settling tanks. Interestingly, the floc size distributions did not vary with position inside the settling tank flocculator. This was an unexpected finding, and seriously questioned the need for a flocculator in the settling tank. It is conjectured that the invariable size distributions were caused by the unique combination of high solids concentration, low shear and zeolite dosing. (C) 2004 Society of Chemical Industry.

Hierarchical indexing structure for efficient similarity search in video retrieval

With the rapid increase in both centralized video archives and distributed WWW video resources, content-based video retrieval is gaining its importance. To support such applications efficiently, content-based video indexing must be addressed. Typically, each video is represented by a sequence of frames. Due to the high dimensionality of frame representation and the large number of frames, video indexing introduces an additional degree of complexity. In this paper, we address the problem of content-based video indexing and propose an efficient solution, called the Ordered VA-File (OVA-File) based on the VA-file. OVA-File is a hierarchical structure and has two novel features: 1) partitioning the whole file into slices such that only a small number of slices are accessed and checked during k Nearest Neighbor (kNN) search and 2) efficient handling of insertions of new vectors into the OVA-File, such that the average distance between the new vectors and those approximations near that position is minimized. To facilitate a search, we present an efficient approximate kNN algorithm named Ordered VA-LOW (OVA-LOW) based on the proposed OVA-File. OVA-LOW first chooses possible OVA-Slices by ranking the distances between their corresponding centers and the query vector, and then visits all approximations in the selected OVA-Slices to work out approximate kNN. The number of possible OVA-Slices is controlled by a user-defined parameter delta. By adjusting delta, OVA-LOW provides a trade-off between the query cost and the result quality. Query by video clip consisting of multiple frames is also discussed. Extensive experimental studies using real video data sets were conducted and the results showed that our methods can yield a significant speed-up over an existing VA-file-based method and iDistance with high query result quality. Furthermore, by incorporating temporal correlation of video content, our methods achieved much more efficient performance.

Website physical activity interventions: preferences of potential users

Information and communication technologies (particularly websites and e-mail) have the potential to deliver health behavior change programs to large numbers of adults at low cost. Controlled trials using these new media to promote physical activity have produced mixed results. User-centered development methods can assist in understanding the preferences of potential participants for website functions and content, and may lead to more effective programs. Eight focus group discussions were conducted with 40 adults after they had accessed a previously trialed physical activity website. The discussions were audio taped, transcribed and interpreted using a themed analysis method. Four key themes emerged: structure, interactivity, environmental context and content. Preferences were expressed for websites that include simple interactive features, together with information on local community activity opportunities. Particular suggestions included online community notice boards, personalized progress charts, e-mail access to expert advice and access to information on specific local physical activity facilities and services. Website physical activity interventions could usefully include personally relevant interactive and environmentally focused features and services identified through a user-centered development process.

Subtypes of Illicit Drug Users: A Latent Class Analysis of Data From an Australian Twin Sample

This article applies methods of latent class analysis (LCA) to data on lifetime illicit drug use in order to determine whether qualitatively distinct classes of illicit drug users can be identified. Self-report data on lifetime illicit drug use (cannabis, stimulants, hallucinogens, sedatives, inhalants, cocaine, opioids and solvents) collected from a sample of 6265 Australian twins (average age 30 years) were analyzed using LCA. Rates of childhood sexual and physical abuse, lifetime alcohol and tobacco dependence, symptoms of illicit drug abuse/dependence and psychiatric comorbidity were compared across classes using multinomial logistic regression. LCA identified a 5-class model: Class 1 (68.5%) had low risks of the use of all drugs except cannabis; Class 2 (17.8%) had moderate risks of the use of all drugs; Class 3 (6.6%) had high rates of cocaine, other stimulant and hallucinogen use but lower risks for the use of sedatives or opioids. Conversely, Class 4 (3.0%) had relatively low risks of cocaine, other stimulant or hallucinogen use but high rates of sedative and opioid use. Finally, Class 5 (4.2%) had uniformly high probabilities for the use of all drugs. Rates of psychiatric comorbidity were highest in the polydrug class although the sedative/opioid class had elevated rates of depression/suicidal behaviors and exposure to childhood abuse. Aggregation of population-level data may obscure important subgroup differences in patterns of illicit drug use and psychiatric comorbidity. Further exploration of a 'self-medicating' subgroup is needed.

Document re-ranking by generality in bio-medical information retrieval

Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.

Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.

On aggregation issues in spatial data management

Large amounts of information can be overwhelming and costly to process, especially when transmitting data over a network. A typical modern Geographical Information System (GIS) brings all types of data together based on the geographic component of the data and provides simple point-and-click query capabilities as well as complex analysis tools. Querying a Geographical Information System, however, can be prohibitively expensive due to the large amounts of data which may need to be processed. Since the use of GIS technology has grown dramatically in the past few years, there is now a need more than ever, to provide users with the fastest and least expensive query capabilities, especially since an approximated 80 % of data stored in corporate databases has a geographical component. However, not every application requires the same, high quality data for its processing. In this paper we address the issues of reducing the cost and response time of GIS queries by preaggregating data by compromising the data accuracy and precision. We present computational issues in generation of multi-level resolutions of spatial data and show that the problem of finding the best approximation for the given region and a real value function on this region, under a predictable error, in general is "NP-complete.

Structured ontology and information retrieval for email search and discovery

This paper discusses an document discovery tool based on formal concept analysis. The program allows users to navigate email using a visual lattice metaphor rather than a tree. It implements a virtual file structure over email where files and entire directories can appear in multiple positions. The content and shape of the lattice formed by the conceptual ontology can assist in email discovery. The system described provides more flexibility in retrieving stored emails than what is normally available in email clients. The paper discusses how conceptual ontologies can leverage traditional document retrieval systems.

Farming under constrained budgets: Practices of agricultural credit users in central Lombok, Indonesia

There has been a long dependency on credit by Indonesian farmers as a result of the lack of capital to apply proper farming practices. This paper describes the farming activities applied by agricultural credit users in Central Lombok, Indonesia. A survey was conducted during July 2001- March 2002 of 65 farmers making use of government or private credit in three villages within the Regency. Data from the farmers were collected using face-to-face, semi-structured interviews. Survey results indicated that on average, farmers had some 20 years experience of farming, were aged 40 years, but lacked of formal education. Their main asset was cropping land with average landholding of 0.69 ha. As a consequence of their capital constraints, farmers were commonly making use of credit to finance their farming activities, including both production of rice as the main crop and secondary crops. Farmers generally applied less than recommended amount of inputs in their farming practices, since the amount of credit they obtained was limited. As a result, their farms become less productive and their repayment capability of loans diminished. Of 65 farmers interviewed, 54 could earn extra income by engaging in a variety of non-farm activities, which contributed on average some 36% to family incomes of over Rp 5 million (A$ 1 thousand). The average credit repayment rate made by agricultural producers was 60%. The repayment made did not always reflect farm production capacity, being sometimes supported by other borrowings. The greater role of credit is not in increasing agricultural production or improving farmers’ income, but in helping them to sustain farm production and their living. Farmers need a bigger amount of credit to make an impact on their livelihood. This should be accompanied by extension services for farmers to enable better use of credit and to change their attitude towards it. As well, farmers require to be equipped with technical and market skills to run a business. Interdisciplinarity, holistic analysis, and an expansion of traditional ‘agricultural’ interests to embrace the span of interests included in rural livelihood, are each critical features of revision of the existing system.