939 resultados para survivorship care models


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Polynomial models are shown to simulate accurately the quadratic and cubic nonlinear interactions (e.g. higher-order spectra) of time series of voltages measured in Chua's circuit. For circuit parameters resulting in a spiral attractor, bispectra and trispectra of the polynomial model are similar to those from the measured time series, suggesting that the individual interactions between triads and quartets of Fourier components that govern the process dynamics are modeled accurately. For parameters that produce the double-scroll attractor, both measured and modeled time series have small bispectra, but nonzero trispectra, consistent with higher-than-second order nonlinearities dominating the chaos.

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Overall, computer models and simulations have a rather disappointing record within the management sciences as a tool for predicting the future. Social and market environments can be influenced by an overwhelming number of variables, and it is therefore difficult to use computer models to make forecasts or to test hypotheses concerning the relationship between individual behaviours and macroscopic outcomes. At the same time, however, advocates of computer models argue that they can be used to overcome the human mind's inability to cope with several complex variables simultaneously or to understand concepts that are highly counterintuitive. This paper seeks to bridge the gap between these two perspectives by suggesting that management research can indeed benefit from computer models by using them to formulate fruitful hypotheses.

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We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

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A time series method for the determination of combustion chamber resonant frequencies is outlined. This technique employs the use of Markov-chain Monte Carlo (MCMC) to infer parameters in a chosen model of the data. The development of the model is included and the resonant frequency is characterised as a function of time. Potential applications for cycle-by-cycle analysis are discussed and the bulk temperature of the gas and the trapped mass in the combustion chamber are evaluated as a function of time from resonant frequency information.

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Language Modeling (LM) has been successfully applied to Information Retrieval (IR). However, most of the existing LM approaches only rely on term occurrences in documents, queries and document collections. In traditional unigram based models, terms (or words) are usually considered to be independent. In some recent studies, dependence models have been proposed to incorporate term relationships into LM, so that links can be created between words in the same sentence, and term relationships (e.g. synonymy) can be used to expand the document model. In this study, we further extend this family of dependence models in the following two ways: (1) Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented; (2) We exploit more sophisticated inferential relationships extracted with Information Flow (IF). Information flow relationships are not simply pairwise term relationships as those used in previous studies, but are between a set of terms and another term. They allow for context-dependent query expansion. Our experiments conducted on TREC collections show that we can obtain large and significant improvements with our approach. This study shows that LM is an appropriate framework to implement effective query expansion.

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In Viet Nam, standards of nursing care fail to meet international competency standards. This increases risks to patient safety (eg. hospital acquired infection), consequently the Ministry of Health identified the need to strengthen nurse education in Viet Nam. This paper presents experiences of a piloted clinical teaching model developed in Ha Noi, to strengthen nurse led institutional capacity for in-service education and clinical teaching. Historically 90% of nursing education was conducted by physicians and professional development in hospitals for nurses was limited. There was minimal communication between hospitals and nursing schools about expectations of students and assessment and quality of the learning experience. As a result when students came to the clinical sites, no-one understood how to plan their learning objectives and utilise teaching and learning approaches appropriate to their level. Therefore student learning outcomes were variable. They focussed on procedures and techniques and “learning how to do” rather than learning how to plan, implement and evaluate patient care. This project is part of a multi-component capacity building program designed to improve nurse education in Viet Nam. The project was funded jointly by Queensland University of Technology (QUT) and the Australian Agency for International Development. Its aim was to develop a collaborative clinically-based model of teaching to create an environment that encourages evidence-based, student-centred clinical learning. Accordingly, strategies introduced promoted clinical teaching of competency based nursing practice utilising the regionally endorsed nurse core competency standards. Thirty nurse teachers from Viet Duc University Hospital and Hanoi Medical College participated in the program. These nurses and nurse teachers undertook face to face education in three workshops, and completed three assessment items. Assessment was applied, where participants integrated the concepts learned in each workshop and completed assessment tasks related to planning, implementing and evaluating teaching in the clinical area. Twenty of these participants were then selected to undertake a two week study tour in Brisbane, Australia where the clinical teaching model was refined and an action plan developed to integrate into both organisations with possible implementation across Viet Nam. Participants on this study tour also experienced clinical teaching and learning at QUT by attending classes held at the university, and were able to visit selected hospitals to experience clinical teaching in these settings as well. Effectiveness of the project was measured throughout the implementation phase and in follow up visits to the clinical site. To date changes have been noted on an individual and organisational level. There is also significant planning underway to incorporate the clinical teaching model developed across the organisation and how this may be implemented in other regions. Two participants have also been involved in disseminating aspects of this approach to clinical teaching in Ho Chi Minh, with further plans for more in-depth dissemination to occur throughout the country.

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Research into complaints handling in the health care system has predominately focused on examining the processes that underpin the organisational systems. An understanding of the cognitive decisions made by patients that influence whether they are satisfied or dissatisfied with the care they are receiving has had limited attention thus far. This study explored the lived experiences of Queensland acute care patients who complained about some aspect of their inpatient stay. A purposive sample of sixteen participants was recruited and interviewed about their experience of making a complaint. The qualitative data gathered through the interview process was subjected to an Interpretative Phenomenological Analysis (IPA) approach, guided by the philosophical influences of Heidegger (1889-1976). As part of the interpretive endeavour of this study, Lazarus’ cognitive emotive model with situational challenge was drawn on to provide a contextual understanding of the emotions experienced by the study participants. Analysis of the research data, aided by Leximancer™ software, revealed a series of relational themes that supported the interpretative data analysis process undertaken. The superordinate thematic statements that emerged from the narratives via the hermeneutic process were ineffective communication, standards of care were not consistent, being treated with disrespect, information on how to complain was not clear, and perceptions of negligence. This study’s goal was to provide health services with information about complaints handling that can help them develop service improvements. The study patients articulated the need for health care system reform; they want to be listened to, to be acknowledged, to be believed, for people to take ownership if they had made a mistake, for mistakes not to occur again, and to receive an apology. For these initiatives to be fully realised, the paradigm shift must go beyond regurgitating complaints data metrics in percentages per patient contact, towards a concerted effort to evaluate what the qualitative complaints data is really saying. An opportunity to identify a more positive and proactive approach in encouraging our patients to complain when they are dissatisfied has the potential to influence improvements.

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Health education in Western Countries has grown considerably in the last decade and this has occurred for a number of reasons. Specifically Universities actively recruit International students as the health workforce becomes global; also it is much easier for students to move and study globally. Internationally there is a health workforce shortage and if students gain a degree in a reputable university their ability to work globally is improved significantly. However, when studying to practice in the health care field the student must undertake clinical practice in an acute or aged care setting. This can be a significant problem for students who are culturally and linguistically diverse in an English speaking country such as Australia. The issues that can arise stem from the language differences where communication, interpretation understanding and reading the cultural norms of the health care setting are major challenges for International students. To assist international students to be successful in their clinical education, an extra curriculum workshop program was developed to provide additional support. The program which runs twice each year includes on-campus interactive workshops that are complemented by targeted support provided for students and clinical staff who are supervising students’ practice experience in the workplace. As this is an English speaking country the workshop is based on practicing reading, writing, listening and speaking, as well as exploring basic health care concepts and cultural differences. This enables students to gain knowledge of and practice interpretation of cultural norms and expectations in a safe environment. This innovative series of interactive workshops in a highly student-centred learning environment combine education with role play and discussion with peers who are supported by culturally aware and competent Educators. Over the years it has been running, the program has been undertaken by an increasing number of students. In 2011, more than 100 students are expected to participate. Student evaluation of the program has confirmed that it has assisted the majority of them to be successful in their clinical studies. Effectiveness of the project is measured throughout the program and in follow up sessions. This ongoing information allows for continuous development of the program that serves to meet individual needs of the International student, the University and Service providers such as the hospitals. This feedback from students regarding their increased comprehension of the Australian colloquial Language, healthcare terminology, critical thinking and clinical skill development and a cultural awareness also enables them to maintain their feelings of self confidence and self esteem.

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Care and decision-making at the end of life that promotes comfort and dignity is widely endorsed by public policy and the law. In ethical analysis of palliative care interventions that are argued potentially to hasten death, these may be deemed to be ethically permissible by the application of the doctrine of double effect, if the doctor’s intention is to relieve pain and not cause death. In part because of the significance of ethics in the development of law in the medical sphere, this doctrine is also likely to be recognized as part of Australia’s common law, although hitherto there have been no cases concerning palliative care brought before a court in Australia to test this. Three Australian States have, nonetheless, created legislative defences that are different from the common law with the intent of clarifying the law, promoting palliative care, and distinguishing it from euthanasia. However, these defences have the potential to provide less protection for doctors administering palliative care. In addition to requiring a doctor to have an appropriate intent, the defences insist on adherence to particular medical practice standards and perhaps require patient consent. Doctors providing end-of-life care in these States need to be aware of these legislative changes. Acting in accordance with the common law doctrine of double effect may not provide legal protection. Similar changes are likely to occur in other States and Territories as there is a trend towards enacting legislative defences that deal with the provision of palliative care.

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Whether the community is looking for “scapegoats” to blame, or seeking more radical and deeper causes, health care managers are in the firing line whenever there are woes in the health care sector. The public has a right to question whether ethics have much influence on the everyday decision making of health care managers. This thesis explores, through a series of published papers, the influence of ethics and other factors on the decision making of health care managers in Australia. Critical review of over 40 years of research on ethical decision making has revealed a large number of influencing factors, but there is a demonstrable lack of a multidimensional approach that measures the combined influences of these factors on managers. This thesis has developed an instrument, the Managerial Ethical Profile (MEP) scale, based on a multidimensional model combining a large number of influencing factors. The MEP scale measures the range of influences on individual managers, and describes the major tendencies by developing a number of empirical profiles derived from a hierarchical cluster analysis. The instrument was developed and refined through a process of pilot studies on academics and students (n=41) and small-business managers (n=41), and then was administered to the larger sample of health care managers (n=441). Results from this study indicate that Australian health care managers draw on a range of ethical frameworks in their everyday decision making, forming the basis of five MEPs (Knights, Guardian Angels, Duty Followers, Defenders, and Chameleons). Results from the study also indicate that the range of individual, organisational, and external factors that influence decision making can be grouped into three major clusters or functions. Cross referencing these functions and other demographic data to the MEPs provides analytical insight into the characteristics of the MEPs. These five profiles summarise existing strengths and weaknesses in managerial ethical decision making. Therefore identifying these profiles not only can contribute to increasing organisational knowledge and self-awareness, but also has clear implications for the design and implementation of ethics education and training in large scale organisations in the health care industry.

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Background: Room ventilation is a key determinant of airborne disease transmission. Despite this, ventilation guidelines in hospitals are not founded on robust scientific evidence related to prevention of airborne transmission. Methods: We sought to assess the effect of ventilation rates on influenza, tuberculosis (TB) and rhinovirus infection risk within three distinct rooms in a major urban hospital; a Lung Function Laboratory, Emergency Department (ED) Negative-pressure Isolation Room and an Outpatient Consultation Room were investigated. Air exchange rate measurements were performed in each room using CO2 as a tracer. Gammaitoni and Nucci’s model was employed to estimate infection risk. Results: Current outdoor air exchange rates in the Lung Function Laboratory and ED Isolation Room limited infection risks to between 0.1 and 3.6%. Influenza risk for individuals entering an Outpatient Consultation Room after an infectious individual departed ranged from 3.6 to 20.7%, depending on the duration for which each person occupied the room. Conclusions: Given the absence of definitive ventilation guidelines for hospitals, air exchange measurements combined with modelling afford a useful means of assessing, on a case-by-case basis, the suitability of room ventilation at preventing airborne disease transmission.

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The research objectives of this thesis were to contribute to Bayesian statistical methodology by contributing to risk assessment statistical methodology, and to spatial and spatio-temporal methodology, by modelling error structures using complex hierarchical models. Specifically, I hoped to consider two applied areas, and use these applications as a springboard for developing new statistical methods as well as undertaking analyses which might give answers to particular applied questions. Thus, this thesis considers a series of models, firstly in the context of risk assessments for recycled water, and secondly in the context of water usage by crops. The research objective was to model error structures using hierarchical models in two problems, namely risk assessment analyses for wastewater, and secondly, in a four dimensional dataset, assessing differences between cropping systems over time and over three spatial dimensions. The aim was to use the simplicity and insight afforded by Bayesian networks to develop appropriate models for risk scenarios, and again to use Bayesian hierarchical models to explore the necessarily complex modelling of four dimensional agricultural data. The specific objectives of the research were to develop a method for the calculation of credible intervals for the point estimates of Bayesian networks; to develop a model structure to incorporate all the experimental uncertainty associated with various constants thereby allowing the calculation of more credible credible intervals for a risk assessment; to model a single day’s data from the agricultural dataset which satisfactorily captured the complexities of the data; to build a model for several days’ data, in order to consider how the full data might be modelled; and finally to build a model for the full four dimensional dataset and to consider the timevarying nature of the contrast of interest, having satisfactorily accounted for possible spatial and temporal autocorrelations. This work forms five papers, two of which have been published, with two submitted, and the final paper still in draft. The first two objectives were met by recasting the risk assessments as directed, acyclic graphs (DAGs). In the first case, we elicited uncertainty for the conditional probabilities needed by the Bayesian net, incorporated these into a corresponding DAG, and used Markov chain Monte Carlo (MCMC) to find credible intervals, for all the scenarios and outcomes of interest. In the second case, we incorporated the experimental data underlying the risk assessment constants into the DAG, and also treated some of that data as needing to be modelled as an ‘errors-invariables’ problem [Fuller, 1987]. This illustrated a simple method for the incorporation of experimental error into risk assessments. In considering one day of the three-dimensional agricultural data, it became clear that geostatistical models or conditional autoregressive (CAR) models over the three dimensions were not the best way to approach the data. Instead CAR models are used with neighbours only in the same depth layer. This gave flexibility to the model, allowing both the spatially structured and non-structured variances to differ at all depths. We call this model the CAR layered model. Given the experimental design, the fixed part of the model could have been modelled as a set of means by treatment and by depth, but doing so allows little insight into how the treatment effects vary with depth. Hence, a number of essentially non-parametric approaches were taken to see the effects of depth on treatment, with the model of choice incorporating an errors-in-variables approach for depth in addition to a non-parametric smooth. The statistical contribution here was the introduction of the CAR layered model, the applied contribution the analysis of moisture over depth and estimation of the contrast of interest together with its credible intervals. These models were fitted using WinBUGS [Lunn et al., 2000]. The work in the fifth paper deals with the fact that with large datasets, the use of WinBUGS becomes more problematic because of its highly correlated term by term updating. In this work, we introduce a Gibbs sampler with block updating for the CAR layered model. The Gibbs sampler was implemented by Chris Strickland using pyMCMC [Strickland, 2010]. This framework is then used to consider five days data, and we show that moisture in the soil for all the various treatments reaches levels particular to each treatment at a depth of 200 cm and thereafter stays constant, albeit with increasing variances with depth. In an analysis across three spatial dimensions and across time, there are many interactions of time and the spatial dimensions to be considered. Hence, we chose to use a daily model and to repeat the analysis at all time points, effectively creating an interaction model of time by the daily model. Such an approach allows great flexibility. However, this approach does not allow insight into the way in which the parameter of interest varies over time. Hence, a two-stage approach was also used, with estimates from the first-stage being analysed as a set of time series. We see this spatio-temporal interaction model as being a useful approach to data measured across three spatial dimensions and time, since it does not assume additivity of the random spatial or temporal effects.

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Velocity jump processes are discrete random walk models that have many applications including the study of biological and ecological collective motion. In particular, velocity jump models are often used to represent a type of persistent motion, known as a “run and tumble”, which is exhibited by some isolated bacteria cells. All previous velocity jump processes are non-interacting, which means that crowding effects and agent-to-agent interactions are neglected. By neglecting these agent-to-agent interactions, traditional velocity jump models are only applicable to very dilute systems. Our work is motivated by the fact that many applications in cell biology, such as wound healing, cancer invasion and development, often involve tissues that are densely packed with cells where cell-to-cell contact and crowding effects can be important. To describe these kinds of high cell density problems using a velocity jump process we introduce three different classes of crowding interactions into a one-dimensional model. Simulation data and averaging arguments lead to a suite of continuum descriptions of the interacting velocity jump processes. We show that the resulting systems of hyperbolic partial differential equations predict the mean behavior of the stochastic simulations very well.

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OBJECTIVES: To identify the prevalence of geriatric syndromes in the premorbid for all syndromes except falls (preadmission), admission, and discharge assessment periods and the incidence of new and significant worsening of existing syndromes at admission and discharge. DESIGN: Prospective cohort study. SETTING: Three acute care hospitals in Brisbane, Australia. PARTICIPANTS: Five hundred seventy-seven general medical patients aged 70 and older admitted to the hospital. MEASUREMENTS: Prevalence of syndromes in the premorbid (or preadmission for falls), admission, and discharge periods; incidence of new syndromes at admission and discharge; and significant worsening of existing syndromes at admission and discharge. RESULTS: The most frequently reported premorbid syndromes were bladder incontinence (44%), impairment in any activity of daily living (ADL) (42%). A high proportion (42%) experienced at least one fall in the 90 days before admission. Two-thirds of the participants experienced between one and five syndromes (cognitive impairment, dependence in any ADL item, bladder and bowel incontinence, pressure ulcer) before, at admission, and at discharge. A majority experienced one or two syndromes during the premorbid (49.4%), admission (57.0%), or discharge (49.0%) assessment period.The syndromes with a higher incidence of significant worsening at discharge (out of the proportion with the syndrome present premorbidly) were ADL limitation (33%), cognitive impairment (9%), and bladder incontinence (8%). Of the syndromes examined at discharge, a higher proportion of patients experienced the following new syndromes at discharge (absent premorbidly): ADL limitation (22%); and bladder incontinence (13%). CONCLUSION: Geriatric syndromes were highly prevalent. Many patients did not return to their premorbid function and acquired new syndromes.

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We propose to use the Tensor Space Modeling (TSM) to represent and analyze the user’s web log data that consists of multiple interests and spans across multiple dimensions. Further we propose to use the decomposition factors of the Tensors for clustering the users based on similarity of search behaviour. Preliminary results show that the proposed method outperforms the traditional Vector Space Model (VSM) based clustering.