A case study of epistemic order in mathematics classroom dialogue

We define epistemic order as the way in which the exchange and development of knowledge takes place in the classroom, breaking this down into a system of three components: epistemic initiative relating to who sets the agenda in classroom dialogue, and how; epistemic appraisal relating to who judges contributions to classroom dialogue, and how; and epistemic framing relating to the terms in which development and exchange of knowledge are represented, particularly in reflexive talk. These components are operationalised in terms of various types of structural and semantic analysis of dialogue. It is shown that a lesson segment displays a multi-layered epistemic order differing from that of conventional classroom recitation.

Job satisfaction among Australian general practitioners and specialists

OBJECTIVE:
To identify patterns of job satisfaction among Australian doctors using latent class analysis, and to determine the relationships of these patterns to personal and professional characteristics so as to improve satisfaction and minimize medical wastage.
METHODS:
MABEL (Medicine in Australia: Balancing Employment and Life) data in 2011 were used. The study collected information on 5764 doctors about their job satisfaction, demographic characteristics, their health, country of medical training, opportunities for professional development and social interaction, taking time off work, views of patients' expectations, unpredictable working hours, hours worked per week, preference to reduce hours and intention to leave the medical workforce.
RESULTS:
Four latent classes of job satisfaction were identified: 5.8% had high job satisfaction; 19.4% had low satisfaction with working hours; 16.1% had high satisfaction with working hours but felt undervalued; and 6.5% had low job satisfaction. Low job satisfaction was associated with reporting poor health, having trained outside Australia, having poor opportunities for professional development and working longer hours. Low satisfaction was associated with a preference to reduce work hours and an intention to leave the medical workforce.
CONCLUSION:
To improve job satisfaction and minimize medical wastage, policies need to address needs of overseas trained doctors, provide continuing professional development and provide good health care for doctors.

The relationship between psychological distress and adolescent polydrug use.

Polydrug use is relatively common among adolescents. Psychological distress is associated with the use of specific drugs, and may be uniquely associated with polydrug use. The purpose of this study was to test the association of psychological distress with polydrug use using a large adolescent sample. The sample consisted of 10,273 students aged 12-17 years from the State of Victoria, Australia. Participants completed frequency measures of tobacco, alcohol, cannabis, inhalant, and other drug use in the past 30 days, and psychological distress. Control variables included age, gender, family socioeconomic status, school suspensions, academic failure, cultural background, and peer drug use. Drug-use classes were derived using latent-class analysis, then the association of psychological distress and controls with drug-use classes was modeled using multinomial ordinal regression. There were 3 distinct classes of drug use: no drug use (47.7%), mainly alcohol use (44.1%), and polydrug use (8.2%). Independent of all controls, psychological distress was higher in polydrug users and alcohol users, relative to nondrug users, and polydrug users reported more psychological distress than alcohol users. Psychological distress was most characteristic of polydrug users, and targeted prevention outcomes may be enhanced by a collateral focus on polydrug use and depression and/or anxiety.

A profile of four patterns of vulnerability to functional decline in older general medicine patients in Victoria, Australia: a cross sectional survey

BACKGROUND: There are limited published data reporting Australian hospitalized elders' vulnerability to functional decline to guide best practice interventions. The objectives of this study were to describe the prevalence of vulnerability to functional decline and explore profiles of vulnerability related to the performance of physical activity in a representative group of elders in a single centre in Victoria, Australia.

METHODS: A cross-sectional survey of patients aged ≥ 70 years (Mean age 82.4, SD 7 years) admitted to a general medical ward of an Australian tertiary-referral metropolitan public hospital from March 2010 to March 2011 (n = 526). Patients were screened using the Vulnerable Elders Survey (VES-13). Distinct typologies of physical difficulties were identified using latent class analysis.

RESULTS: Most elders scored ≥3/10 on the VES-13 and were rated vulnerable to functional decline (n = 480, 89.5 %). Four distinct classes of physical difficulty were identified: 1) Elders with higher physical functioning (n = 114, 21.7 %); 2) Ambulant elders with diminished strength (n = 24, 4.6 %); 3) Elders with impaired mobility, strength and ability to stoop (n = 267, 50.8 %) and 4) Elders with extensive physical impairment (n = 121, 23 %) Vulnerable elders were distributed through all classes.

CONCLUSIONS: Older general medicine patients in Victoria, Australia, are highly vulnerable to functional decline. We identified four distinct patterns of physical difficulties associated with vulnerability to functional decline that can inform health service planning, delivery and education.

Exploring the relationship between patterns of physical activity, sedentary behaviour and cardiometabolic health in middle-aged Irish adults

Background and Study Rationale Being physically active is a major contributor to both physical and mental health. More specifically, being physically active lowers risk of coronary heart disease, high blood pressure, stroke, metabolic syndrome (MetS), diabetes, certain cancers and depression, and increases cognitive function and wellbeing. The physiological mechanisms that occur in response to physical activity and the impact of total physical activity and sedentary behaviour on cardiometabolic health have been extensively studied. In contrast, limited data evaluating the specific effects of daily and weekly patterns of physical behaviour on cardiometabolic health exist. Additionally, no other study has examined interrelated patterns and minute-by-minute accumulation of physical behaviour throughout the day across week days in middle-aged adults. Study Aims The overarching aims of this thesis are firstly to describe patterns of behaviour throughout the day and week, and secondly to explore associations between these patterns and cardiometabolic health in a middle-aged population. The specific objectives are to: 1 Compare agreement between the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and GENEActiv accelerometer-derived moderate-to-vigorous (MVPA) activity and secondly to compare their associations with a range of cardiometabolic and inflammatory markers in middle-aged adults. 2 Determine a suitable monitoring frame needed to reliably capture weekly, accelerometer-measured, activity in our population. 3 Identify groups of participants who have similar weekly patterns of physical behaviour, and determine if underlying patterns of cardiometabolic profiles exist among these groups. 4 Explore the variation of physical behaviour throughout the day to identify whether daily patterns of physical behaviour vary by cardiometabolic health. Methods All results in this thesis are based on data from a subsample of the Mitchelstown Cohort; 475 (46.1% males; mean aged 59.7±5.5 years) middle-aged Irish adults. Subjective physical activity levels were assessed using the IPAQ-SF. Participants wore the wrist GENEActiv accelerometer for 7 consecutive days. Data was collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised based on validated cut-offs. Data on cardiometabolic and inflammatory markers was collected according to standard protocol. Cardiometabolic outcomes (obesity, diabetes, hypertension and MetS) were defined according to internationally recognised definitions by World Health Organisation (WHO) and Irish Diabetes Federation (IDF). Results The results of the first chapter suggest that the IPAQ-SF lacks the sensitivity to assess patterning of activity and guideline adherence and assessing the relationship with cardiometabolic and inflammatory markers. Furthermore, GENEActiv accelerometer-derived MVPA appears to be better at detecting relationships with cardiometabolic and inflammatory markers. The second chapter examined variations in day-to-day physical behaviour levels between- and within-subjects. The main findings were that Sunday differed from all other days in the week for sedentary behaviour and light activity and that a large within-subject variation across days of the week for vigorous activity exists. Our data indicate that six days of monitoring, four weekdays plus Saturday and Sunday, are required to reliably estimate weekly habitual activity in all activity intensities. In the next chapter, latent profile analysis of weekly, interrelated patterns of physical behaviour identified four distinct physical behaviour patterns; Sedentary Group (15.9%), Sedentary; Lower Activity Group (28%), Sedentary; Higher Activity Group (44.2%) and a Physically Active Group (11.9%). Overall the Sedentary Group had poorer outcomes, characterised by unfavourable cardiometabolic and inflammatory profiles. The remaining classes were characterised by healthier cardiometabolic profiles with lower sedentary behaviour levels. The final chapter, which aimed to compare daily cumulative patterns of minute-by-minute physical behaviour intensities across those with and without MetS, revealed significant differences in weekday and weekend day MVPA. In particular, those with MetS start accumulating MVPA later in the day and for a shorted day period. Conclusion In conclusion, the results of this thesis add to the evidence base regards an optimal monitoring period for physical behaviour measurement to accurately capture weekly physical behaviour patterns. In addition, the results highlight whether weekly and daily distribution of activity is associated with cardiometabolic health and inflammatory profiles. The key findings of this thesis demonstrate the importance of daily and weekly physical behaviour patterning of activity intensity in the context of cardiometabolic health risk. In addition, these findings highlight the importance of using physical behaviour patterns of free-living adults observed in a population-based study to inform and aid health promotion activity programmes and primary care prevention and treatment strategies and development of future tailored physical activity based interventions.

The complexity in the implementation process of empowerment-based chronic kidney care: a case study

Background: This study is part of an interactive improvement intervention aimed to facilitate empowerment-based chronic kidney care using data from persons with CKD and their family members. There are many challenges to implementing empowerment-based care, and it is therefore necessary to study the implementation process. The aim of this study was to generate knowledge regarding the implementation process of an improvement intervention of empowerment for those who require chronic kidney care. Methods: A prospective single qualitative case study was chosen to follow the process of the implementation over a two year period. Twelve health care professionals were selected based on their various role(s) in the implementation of the improvement intervention. Data collection comprised of digitally recorded project group meetings, field notes of the meetings, and individual interviews before and after the improvement project. These multiple data were analyzed using qualitative latent content analysis. Results: Two facilitator themes emerged: Moving spirit and Encouragement. The healthcare professionals described a willingness to individualize care and to increase their professional development in the field of chronic kidney care. The implementation process was strongly reinforced by both the researchers working interactively with the staff, and the project group. One theme emerged as a barrier: the Limitations of the organization. Changes in the organization hindered the implementation of the intervention throughout the study period, and the lack of interplay in the organization most impeded the process. Conclusions: The findings indicated the complexity of maintaining a sustainable and lasting implementation over a period of two years. Implementing empowerment-based care was found to be facilitated by the cooperation between all involved healthcare professionals. Furthermore, long-term improvement interventions need strong encouragement from all levels of the organization to maintain engagement, even when it is initiated by the health care professionals themselves.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Exploiting coherence for the simultaneous discovery of latent facets and associated sentiments

Facet-based sentiment analysis involves discovering the latent facets, sentiments and their associations. Traditional facet-based sentiment analysis algorithms typically perform the various tasks in sequence, and fail to take advantage of the mutual reinforcement of the tasks. Additionally,inferring sentiment levels typically requires domain knowledge or human intervention. In this paper, we propose aseries of probabilistic models that jointly discover latent facets and sentiment topics, and also order the sentiment topics with respect to a multi-point scale, in a language and domain independent manner. This is achieved by simultaneously capturing both short-range syntactic structure and long range semantic dependencies between the sentiment and facet words. The models further incorporate coherence in reviews, where reviewers dwell on one facet or sentiment level before moving on, for more accurate facet and sentiment discovery. For reviews which are supplemented with ratings, our models automatically order the latent sentiment topics, without requiring seed-words or domain-knowledge. To the best of our knowledge, our work is the first attempt to combine the notions of syntactic and semantic dependencies in the domain of review mining. Further, the concept of facet and sentiment coherence has not been explored earlier either. Extensive experimental results on real world review data show that the proposed models outperform various state of the art baselines for facet-based sentiment analysis.

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.

High-level concept annotation using ontology and probabilistic inference

Image annotation is a significant step towards semantic based image retrieval. Ontology is a popular approach for semantic representation and has been intensively studied for multimedia analysis. However, relations among concepts are seldom used to extract higher-level semantics. Moreover, the ontology inference is often crisp. This paper aims to enable sophisticated semantic querying of images, and thus contributes to 1) an ontology framework to contain both visual and contextual knowledge, and 2) a probabilistic inference approach to reason the high-level concepts based on different sources of information. The experiment on a natural scene database from LabelMe database shows encouraging results.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

Interactive content analysis : evaluating interactive variants of non-negative Matrix Factorisation and Latent Dirichlet Allocation as qualitative content analysis aids

This thesis addressed issues that have prevented qualitative researchers from using thematic discovery algorithms. The central hypothesis evaluated whether allowing qualitative researchers to interact with thematic discovery algorithms and incorporate domain knowledge improved their ability to address research questions and trust the derived themes. Non-negative Matrix Factorisation and Latent Dirichlet Allocation find latent themes within document collections but these algorithms are rarely used, because qualitative researchers do not trust and cannot interact with the themes that are automatically generated. The research determined the types of interactivity that qualitative researchers require and then evaluated interactive algorithms that matched these requirements. Theoretical contributions included the articulation of design guidelines for interactive thematic discovery algorithms, the development of an Evaluation Model and a Conceptual Framework for Interactive Content Analysis.

Probabilistic power system contingency analysis considering wind

This thesis was a step forward in developing probabilistic assessment of power system response to faults subject to intermittent generation by renewable energy. It has investigated the wind power fluctuation effect on power system stability, and the developed fast estimation process has demonstrated the feasibility for real-time implementation. A better balance between power network security and efficiency can be achieved based on this research outcome.