The effectiveness of dietary counselling versus oral nutrition supplements in improving nutritional intake in malnourished patients with Chronic Obstructive Pulmonary Disease (COPD)

The evidence for nutritional support in COPD is almost entirely based on oral nutritional supplements (ONS) yet despite this dietary counseling and food fortification (DA) are often used as the first line treatment for malnutrition. This study aimed to investigate the effectiveness of ONS vs. DA in improving nutritional intake in malnourished outpatients with COPD. 70 outpatients (BMI 18.4 SD 1.6 kg/m2, age 73 SD 9 years, severe COPD) were randomised to receive a 12-week intervention of either ONS or DA (n 33 ONS vs. n 37 DA). Paired t-test analysis revealed total energy intakes significantly increased with ONS at week 6 (+302 SD 537 kcal/d; p = 0.002), with a slight reduction at week 12 (+243 SD 718 kcal/d; p = 0.061) returning to baseline levels on stopping supplementation. DA resulted in small increases in energy that only reached significance 3 months post-intervention (week 6: +48 SD 623 kcal/d, p = 0.640; week 12: +157 SD 637 kcal/d, p = 0.139; week 26: +247 SD 592 kcal/d, p = 0.032). Protein intake was significantly higher in the ONS group at both week 6 and 12 (ONS: +19.0 SD 25.0 g/d vs. DA: +1.0 SD 13.0 g/d; p = 0.033 ANOVA) but no differences were found at week 26. Vitamin C, Iron and Zinc intakes significantly increased only in the ONS group. ONS significantly increased energy, protein and several micronutrient intakes in malnourished COPD patients but only during the period of supplementation. Trials investigating the effects of combined nutritional interventions are required.

Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering

This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.

Can skin cancer prevention and early detection be improved via mobile phone text messaging? a randomised, attention-control trial.

Objective. To test the impact of a theory-based, SMS (text message)-delivered behavioural intervention (Healthy Text) targeting sun protection or skin self-examination behaviours compared to attention-control. Method. Overall, 546 participants aged 18–42 years were randomised using a computer-generated number list to the skin self-examination (N = 176), sun protection (N = 187), or attention-control (N = 183) text messages group. Each group received 21 text messages about their assigned topic over 12 months (12 weekly messages for three months, then monthly messages for the next nine months). Data was collected via telephone survey at baseline, three-, and 12-months across Queensland from January 2012 to August 2013. Results. One year after baseline, the sun protection (mean change 0.12; P = 0.030) and skin self-examination groups (mean change 0.12; P = 0.035) had significantly greater improvement in their sun protection habits (SPH) index compared to the attention-control group (reference mean change 0.02). The increase in the proportion of participants who reported any skin self-examination from baseline to 12 months was significantly greater in the skin self-examination intervention group (103/163; 63%; P < 0.001) than the sun protection (83/173; 48%), or attention-control (65/165; 36%) groups. There was no significant effect of the intervention for participants who self-reported whole-body skin self-examination, sun tanning behaviour, or sunburn behaviours. Conclusion. The Healthy Text intervention was effective in inducing significant improvements in sun protection and any type of skin self-examination behaviours.

Using automated text analysis to evaluate students’ conceptual understanding

Background: A major challenge for assessing students’ conceptual understanding of STEM subjects is the capacity of assessment tools to reliably and robustly evaluate student thinking and reasoning. Multiple-choice tests are typically used to assess student learning and are designed to include distractors that can indicate students’ incomplete understanding of a topic or concept based on which distractor the student selects. However, these tests fail to provide the critical information uncovering the how and why of students’ reasoning for their multiple-choice selections. Open-ended or structured response questions are one method for capturing higher level thinking, but are often costly in terms of time and attention to properly assess student responses. Purpose: The goal of this study is to evaluate methods for automatically assessing open-ended responses, e.g. students’ written explanations and reasoning for multiple-choice selections. Design/Method: We incorporated an open response component for an online signals and systems multiple-choice test to capture written explanations of students’ selections. The effectiveness of an automated approach for identifying and assessing student conceptual understanding was evaluated by comparing results of lexical analysis software packages (Leximancer and NVivo) to expert human analysis of student responses. In order to understand and delineate the process for effectively analysing text provided by students, the researchers evaluated strengths and weakness for both the human and automated approaches. Results: Human and automated analyses revealed both correct and incorrect associations for certain conceptual areas. For some questions, that were not anticipated or included in the distractor selections, showing how multiple-choice questions alone fail to capture the comprehensive picture of student understanding. The comparison of textual analysis methods revealed the capability of automated lexical analysis software to assist in the identification of concepts and their relationships for large textual data sets. We also identified several challenges to using automated analysis as well as the manual and computer-assisted analysis. Conclusions: This study highlighted the usefulness incorporating and analysing students’ reasoning or explanations in understanding how students think about certain conceptual ideas. The ultimate value of automating the evaluation of written explanations is that it can be applied more frequently and at various stages of instruction to formatively evaluate conceptual understanding and engage students in reflective

Can synchronous online counselling increase uptake of counselling services in secondary schools and what can promote or inhibit implementation?

This research showed that online counselling has the potential to increase the help-seeking of secondary school students - especially those who suffer from high levels of psychological distress. An investigation of why school counsellors are currently reluctant to provide an online counselling service identified a number of barriers to implementing such a potentially vital service. Response to focus groups and surveys completed by students and school counsellors indicated that more distressed students prefer to use online counselling and they would use it for sensitive topics. School counsellors remain concerned about effectiveness, ethical, legal and privacy issues as well as potential misuse of the service. Recommendations for implementation are made.

User preferences for text message-delivered skin cancer prevention and early detection

Evidence is needed for the acceptability and user preferences of receiving skin cancer-related text messages. We prepared 27 questions to evaluate attitudes, satisfaction with program characteristics such as timing and spacing, and overall satisfaction with the Healthy Text program in young adults. Within this randomised controlled trial (age 18-42 years), 546 participants were assigned to one of three Healthy Text message groups; sun protection, skin self-examination, or attention-control. Over a 12-month period, 21 behaviour-specific text messages were sent to each group. Participants’ preferences were compared between the two interventions and control group at the 12-month follow-up telephone interview. In all three groups, participants reported the messages were easy to understand (98%), provided good suggestions or ideas (88%), and were encouraging (86%) and informative (85%) with little difference between the groups. The timing of the texts was received positively (92%); however, some suggestions for frequency or time of day the messages were received from 8% of participants. Participants in the two intervention groups found their messages more informative, and triggering behaviour change compared to control. Text messages about skin cancer prevention and early detection are novel and acceptable to induce behaviour change in young adults.

Relevance feature discovery for text mining

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

Analysing reflective text for learning analytics : an approach using anomaly recontextualisation

Reflective writing is an important learning task to help foster reflective practice, but even when assessed it is rarely analysed or critically reviewed due to its subjective and affective nature. We propose a process for capturing subjective and affective analytics based on the identification and recontextualisation of anomalous features within reflective text. We evaluate 2 human supervised trials of the process, and so demonstrate the potential for an automated Anomaly Recontextualisation process for Learning Analytics.

Parentification: Counselling talk on a helpline for children and young people

This chapter investigates counselling interactions where young clients talk about their experiences of taking on family responsibilities normatively associated with parental roles. In research counselling literature, practices where relationships in families operate so that there is a reversal of roles, with children managing the households and caring for parents and siblings, is described as parentification. Parentification is used in the counselling literature as a clinician/researcher term, which we ‘respecify’ (Garfinkel, 1991) the tem by beginning with an investigation of young clients’ own accounts of being an adult or parent and how counsellors orient to these accounts. As well as providing understandings of how young people propose accounts of their experiences of adult-child role reversal, the chapter contributes to understanding how children and young people use the resources of counselling helplines, and how counselors can communicate effectively with children and young people.

Locating Indigenous Australia within community development practice : clients, consumers or change makers?

The philosophical promise of community development to “resource and empower people so that they can collectively control their own destinies” (Kenny 1996:104) is no doubt alluring to Indigenous Australia. Given the historical and contemporary experiences of colonial control and surveillance of Aboriginal bodies, alongside the continuing experiences of socio-economic disadvantage, community development reaffirms the aspirational goal of Indigenous Australians for self-determination. Self-determination as a national policy agenda for Indigenous Australians emerged in the 1970s and saw the establishment of a wide range of Aboriginal community-controlled services (Tsey et al 2012). Sullivan (2010:4) argues that the Aboriginal community controlled service sector during this time has, and continues to be, instrumental to advancing the plight of Indigenous Australians both materially and politically. Yet community development and self-determination remain highly problematic and contested in how they manifest in Indigenous social policy agendas and in practice (Hollinsworth 1996; Martin 2003; McCausland 2005; Moreton-Robinson 2009). Moreton-Robinson (2009:68) argues that a central theme underpinning these tensions is a reading of Indigeneity in which Aboriginal and Torres Strait Islander people, behaviours, cultures, and communities are pathologised as “dysfunctional” thus enabling assertions that Indigenous people are incapable of managing their own affairs. This discourse distracts us from the “strategies and tactics of patriarchal white sovereignty” that inhibit the “state’s earlier policy of self-determination” (Moreton-Robinson 2009:68). We acknowledge the irony of community development espoused by Ramirez above (1990), that the least resourced are expected to be most resourceful.; however, we wish to interrogate the processes that inhibit Indigenous participation and control of our own affairs rather than further interrogate Aboriginal minds as uneducated, incapable and/or impaired...

Machine learning approaches to analysing textual injury surveillance data: A systematic review

Objective To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Design Systematic review. Data sources The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. Selection criteria For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. Methods The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Results Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. Conclusions The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field.

Text mining for screening efficiency? Testing within a Cochrane public health review

Background The requirement for dual screening of titles and abstracts to select papers to examine in full text can create a huge workload, not least when the topic is complex and a broad search strategy is required, resulting in a large number of results. An automated system to reduce this burden, while still assuring high accuracy, has the potential to provide huge efficiency savings within the review process. Objectives To undertake a direct comparison of manual screening with a semi‐automated process (priority screening) using a machine classifier. The research is being carried out as part of the current update of a population‐level public health review. Methods Authors have hand selected studies for the review update, in duplicate, using the standard Cochrane Handbook methodology. A retrospective analysis, simulating a quasi‐‘active learning’ process (whereby a classifier is repeatedly trained based on ‘manually’ labelled data) will be completed, using different starting parameters. Tests will be carried out to see how far different training sets, and the size of the training set, affect the classification performance; i.e. what percentage of papers would need to be manually screened to locate 100% of those papers included as a result of the traditional manual method. Results From a search retrieval set of 9555 papers, authors excluded 9494 papers at title/abstract and 52 at full text, leaving 9 papers for inclusion in the review update. The ability of the machine classifier to reduce the percentage of papers that need to be manually screened to identify all the included studies, under different training conditions, will be reported. Conclusions The findings of this study will be presented along with an estimate of any efficiency gains for the author team if the screening process can be semi‐automated using text mining methodology, along with a discussion of the implications for text mining in screening papers within complex health reviews.

Decision boundary setting and classifier combination for text classification

This thesis presents a promising boundary setting method for solving challenging issues in text classification to produce an effective text classifier. A classifier must identify boundary between classes optimally. However, after the features are selected, the boundary is still unclear with regard to mixed positive and negative documents. A classifier combination method to boost effectiveness of the classification model is also presented. The experiments carried out in the study demonstrate that the proposed classifier is promising.