Prescribing of Psychoactive Drugs for Older People in Nursing Homes: An Analysis of Treatment Culture

Background There is increasing interest in how culture may affect the quality of healthcare services, and previous research has shown that ‘treatment culture’—of which there are three categories (resident centred, ambiguous and traditional)—in a nursing home may influence prescribing of psychoactive medications. Objective The objective of this study was to explore and understand treatment culture in prescribing of psychoactive medications for older people with dementia in nursing homes. Method Six nursing homes—two from each treatment culture category—participated in this study. Qualitative data were collected through semi-structured interviews with nursing home staff and general practitioners (GPs), which sought to determine participants’ views on prescribing and administration of psychoactive medication, and their understanding of treatment culture and its potential influence on prescribing of psychoactive drugs. Following verbatim transcription, the data were analysed and themes were identified, facilitated by NVivo and discussion within the research team. Results Interviews took place with five managers, seven nurses, 13 care assistants and two GPs. Four themes emerged: the characteristics of the setting, the characteristics of the individual, relationships and decision making. The characteristics of the setting were exemplified by views of the setting, daily routines and staff training. The characteristics of the individual were demonstrated by views on the personhood of residents and staff attitudes. Relationships varied between staff within and outside the home. These relationships appeared to influence decision making about prescribing of medications. The data analysis found that each home exhibited traits that were indicative of its respective assigned treatment culture. Conclusion Nursing home treatment culture appeared to be influenced by four main themes. Modification of these factors may lead to a shift in culture towards a more flexible, resident-centred culture and a reduction in prescribing and use of psychoactive medication. 

Maintaining legacy data: saving Belfast Harbour (UK) tide-gauge data (1902-2010)

Tide gauge data are identified as legacy data given the radical transition between observation method and required output format associated with tide gauges over the 20th-century. Observed water level variation through tide-gauge records is regarded as the only significant basis for determining recent historical variation (decade to century) in mean sea-level and storm surge. There are limited tide gauge records that cover the 20th century, such that the Belfast (UK) Harbour tide gauge would be a strategic long-term (110 years) record, if the full paper-based records (marigrams) were digitally restructured to allow for consistent data analysis. This paper presents the methodology of extracting a consistent time series of observed water levels from the 5 different Belfast Harbour tide gauges’ positions/machine types, starting late 1901. Tide-gauge data was digitally retrieved from the original analogue (daily) records by scanning the marigrams and then extracting the sequential tidal elevations with graph-line seeking software (Ungraph™). This automation of signal extraction allowed the full Belfast series to be retrieved quickly, relative to any manual x–y digitisation of the signal. Restructuring variably lengthed tidal data sets to a consistent daily, monthly and annual file format was undertaken by project-developed software: Merge&Convert and MergeHYD allow consistent water level sampling both at 60 min (past standard) and 10 min intervals, the latter enhancing surge measurement. Belfast tide-gauge data have been rectified, validated and quality controlled (IOC 2006 standards). The result is a consistent annual-based legacy data series for Belfast Harbour that includes over 2 million tidal-level data observations.

Analysis of the Market Potential of Syrian Organic Fruit and Vegetables for Exports to Germany

Syria has been a major producer and exporter of fresh fruit and vegetables (FFV) in the Arabic region. Prior to 2011, Syrian FFV were mainly exported to the neighbouring countries, the Gulf States and Northern Africa as well as to Eastern European countries. Although the EU is potentially one of the most profitable markets of high quality FFV (such as organic ones) in the world, Syrian exports of FFV to Western European countries like Germany have been small. It could be a lucrative opportunity for Syrian growers and exporters of FFV to export organic products to markets such as Germany, where national production is limited to a few months due to climatic conditions. Yet, the organic sector in Syria is comparatively young and only a very small area of FFV is certified according to EU organic regulations. Up to the author’s knowledge, little was known about Syrian farmers’ attitudes towards organic FFV production. There was also no study so far that explored and analysed the determining factors for organic FFV adoption among Syrian farmers as well as the exports of these products to the EU markets. The overarching aim of the present dissertation focused on exploring and identifying the market potential of Syrian exports of organic FFV to Germany. The dissertation was therefore concerned with three main objectives: (i) to explore if German importers and wholesalers of organic FFV see market opportunities for Syrian organic products and what requirements in terms of quality and quantity they have, (ii) to determine the obstacles Syrian producers and exporters face when exporting agricultural products to Germany, and (iii) to investigate whether Syrian farmers of FFV can imagine converting their farms to organic production as well as the underlying reasons why they do so or not. A twofold methodological approach with expert interviews and a farmer survey were used in this dissertation to address the abovementioned objectives. While expert interviews were conducted with German and Syrian wholesalers of (organic) FFV in 2011 (9 interviews each), the farmer survey was administrated with 266 Syrian farmers of FFV in the main region for the production of FFV (i.e. the coastal region) from November 2012 till May 2013. For modelling farmers’ decisions to adopt organic farming, the Theory of Planned Behaviour as theoretical framework and Partial Least Squares Structural Equation Modelling as the main method for data analysis were used in this study. The findings of this dissertation yield implications for the different stakeholders (governmental institutions and NGOs, farmers, exporters, wholesalers, etc.) who are interested in prompting the Syrian export of organic products. Based on the empirical results and a literature review, an action plan to promote Syrian production and export of organic products was developed which can help in the post-war period in Syria at improving the organic sector.

LDA-TM : A two-step approach to twitter topic data clustering

The Twitter System is the biggest social network in the world, and everyday millions of tweets are posted and talked about, expressing various views and opinions. A large variety of research activities have been conducted to study how the opinions can be clustered and analyzed, so that some tendencies can be uncovered. Due to the inherent weaknesses of the tweets - very short texts and very informal styles of writing - it is rather hard to make an investigation of tweet data analysis giving results with good performance and accuracy. In this paper, we intend to attack the problem from another aspect - using a two-layer structure to analyze the twitter data: LDA with topic map modelling. The experimental results demonstrate that this approach shows a progress in twitter data analysis. However, more experiments with this method are expected in order to ensure that the accurate analytic results can be maintained.

Data-Adaptive Modeling using Convex Regression

Thesis (Ph.D.)--University of Washington, 2016-08

“It’s the People That Drive It”: Motivating and Supporting Collaborative Data Use Practices in Three Teacher Education Programs

Thesis (Ph.D.)--University of Washington, 2016-08

How do Young People with ADHD Perceive their Condition: An Interpretative Phenomenological Analysis

Attention Deficit Hyperactivity Disorder (ADHD) is one the most prevalent of childhood diagnoses. There is limited research available from the perspective of the child or young person with ADHD. The current research explored how young people perceive ADHD. A secondary aim of the study was to explore to what extent they identify with ADHD. Five participants took part in this study. Their views were explored using semi-structured interviews guided by methods from Personal Construct Psychology. The data was analysed using Interpretative Phenomenological Analysis (IPA). Data analysis suggests that the young people’s views of ADHD are complex and, at times, contradictory. Four super-ordinate themes were identified: What is ADHD?, The role and impact of others on the experience of ADHD, Identity conflict and My relationship with ADHD. The young people’s contradictory views on ADHD are reflective of portrayals of ADHD in the media. A power imbalance was also identified where the young people perceive that they play a passive role in the management of their treatment. Finally, the young people’s accounts revealed a variety of approaches taken to make sense of their condition.

Multi-Disciplinary Applications of Oceanographic Geophysical Data Collection

Thesis (Ph.D.)--University of Washington, 2016-08

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

Statistical modeling of protein lysate array data

The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.

Data-Aware Scheduling in Datacenters

Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.

Usefulness of social tagging in organizing and providing access to the web: An analysis of indexing consistency and quality

This dissertation research points out major challenging problems with current Knowledge Organization (KO) systems, such as subject gateways or web directories: (1) the current systems use traditional knowledge organization systems based on controlled vocabulary which is not very well suited to web resources, and (2) information is organized by professionals not by users, which means it does not reflect intuitively and instantaneously expressed users’ current needs. In order to explore users’ needs, I examined social tags which are user-generated uncontrolled vocabulary. As investment in professionally-developed subject gateways and web directories diminishes (support for both BUBL and Intute, examined in this study, is being discontinued), understanding characteristics of social tagging becomes even more critical. Several researchers have discussed social tagging behavior and its usefulness for classification or retrieval; however, further research is needed to qualitatively and quantitatively investigate social tagging in order to verify its quality and benefit. This research particularly examined the indexing consistency of social tagging in comparison to professional indexing to examine the quality and efficacy of tagging. The data analysis was divided into three phases: analysis of indexing consistency, analysis of tagging effectiveness, and analysis of tag attributes. Most indexing consistency studies have been conducted with a small number of professional indexers, and they tended to exclude users. Furthermore, the studies mainly have focused on physical library collections. This dissertation research bridged these gaps by (1) extending the scope of resources to various web documents indexed by users and (2) employing the Information Retrieval (IR) Vector Space Model (VSM) - based indexing consistency method since it is suitable for dealing with a large number of indexers. As a second phase, an analysis of tagging effectiveness with tagging exhaustivity and tag specificity was conducted to ameliorate the drawbacks of consistency analysis based on only the quantitative measures of vocabulary matching. Finally, to investigate tagging pattern and behaviors, a content analysis on tag attributes was conducted based on the FRBR model. The findings revealed that there was greater consistency over all subjects among taggers compared to that for two groups of professionals. The analysis of tagging exhaustivity and tag specificity in relation to tagging effectiveness was conducted to ameliorate difficulties associated with limitations in the analysis of indexing consistency based on only the quantitative measures of vocabulary matching. Examination of exhaustivity and specificity of social tags provided insights into particular characteristics of tagging behavior and its variation across subjects. To further investigate the quality of tags, a Latent Semantic Analysis (LSA) was conducted to determine to what extent tags are conceptually related to professionals’ keywords and it was found that tags of higher specificity tended to have a higher semantic relatedness to professionals’ keywords. This leads to the conclusion that the term’s power as a differentiator is related to its semantic relatedness to documents. The findings on tag attributes identified the important bibliographic attributes of tags beyond describing subjects or topics of a document. The findings also showed that tags have essential attributes matching those defined in FRBR. Furthermore, in terms of specific subject areas, the findings originally identified that taggers exhibited different tagging behaviors representing distinctive features and tendencies on web documents characterizing digital heterogeneous media resources. These results have led to the conclusion that there should be an increased awareness of diverse user needs by subject in order to improve metadata in practical applications. This dissertation research is the first necessary step to utilize social tagging in digital information organization by verifying the quality and efficacy of social tagging. This dissertation research combined both quantitative (statistics) and qualitative (content analysis using FRBR) approaches to vocabulary analysis of tags which provided a more complete examination of the quality of tags. Through the detailed analysis of tag properties undertaken in this dissertation, we have a clearer understanding of the extent to which social tagging can be used to replace (and in some cases to improve upon) professional indexing.

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.