Good secrets, bad secrets : disclosure in children’s picture books about sexual child abuse

Well over 50 picture books have been published for children on the topic of sexual child abuse (Lampert & Walsh, 2010) many with the aim of teaching their very young readers how to tell the difference between good and bad secrets. This paper looks at three recent picture books for how they focus on disclosure as an end point.

The relationship between chronic disease self-efficacy and nutritional status, functional ability and quality of life in older adults at risk of hospital readmission

Background and significance: Older adults with chronic diseases are at increasing risk of hospital admission and readmission. Approximately 75% of adults have at least one chronic condition, and the odds of developing a chronic condition increases with age. Chronic diseases consume about 70% of the total Australian health expenditure, and about 59% of hospital events for chronic conditions are potentially preventable. These figures have brought to light the importance of the management of chronic disease among the growing older population. Many studies have endeavoured to develop effective chronic disease management programs by applying social cognitive theory. However, limited studies have focused on chronic disease self-management in older adults at high risk of hospital readmission. Moreover, although the majority of studies have covered wide and valuable outcome measures, there is scant evidence on examining the fundamental health outcomes such as nutritional status, functional status and health-related quality of life. Aim: The aim of this research was to test social cognitive theory in relation to self-efficacy in managing chronic disease and three health outcomes, namely nutritional status, functional status, and health-related quality of life, in older adults at high risk of hospital readmission. Methods: A cross-sectional study design was employed for this research. Three studies were undertaken. Study One examined the nutritional status and validation of a nutritional screening tool; Study Two explored the relationships between participants. characteristics, self-efficacy beliefs, and health outcomes based on the study.s hypothesized model; Study Three tested a theoretical model based on social cognitive theory, which examines potential mechanisms of the mediation effects of social support and self-efficacy beliefs. One hundred and fifty-seven patients aged 65 years and older with a medical admission and at least one risk factor for readmission were recruited. Data were collected from medical records on demographics, medical history, and from self-report questionnaires. The nutrition data were collected by two registered nurses. For Study One, a contingency table and the kappa statistic was used to determine the validity of the Malnutrition Screening Tool. In Study Two, standard multiple regression, hierarchical multiple regression and logistic regression were undertaken to determine the significant influential predictors for the three health outcome measures. For Study Three, a structural equation modelling approach was taken to test the hypothesized self-efficacy model. Results: The findings of Study One suggested that a high prevalence of malnutrition continues to be a concern in older adults as the prevalence of malnutrition was 20.6% according to the Subjective Global Assessment. Additionally, the findings confirmed that the Malnutrition Screening Tool is a valid nutritional screening tool for hospitalized older adults at risk of readmission when compared to the Subjective Global Assessment with high sensitivity (94%), and specificity (89%) and substantial agreement between these two methods (k = .74, p < .001; 95% CI .62-.86). Analysis data for Study Two found that depressive symptoms and perceived social support were the two strongest influential factors for self-efficacy in managing chronic disease in a hierarchical multiple regression. Results of multivariable regression models suggested advancing age, depressive symptoms and less tangible support were three important predictors for malnutrition. In terms of functional status, a standard regression model found that social support was the strongest predictor for the Instrumental Activities of Daily Living, followed by self-efficacy in managing chronic disease. The results of standard multiple regression revealed that the number of hospital readmission risk factors adversely affected the physical component score, while depressive symptoms and self-efficacy beliefs were two significant predictors for the mental component score. In Study Three, the results of the structural equation modelling found that self-efficacy partially mediated the effect of health characteristics and depression on health-related quality of life. The health characteristics had strong direct effects on functional status and body mass index. The results also indicated that social support partially mediated the relationship between health characteristics and functional status. With regard to the joint effects of social support and self-efficacy, social support fully mediated the effect of health characteristics on self-efficacy, and self-efficacy partially mediated the effect of social support on functional status and health-related quality of life. The results also demonstrated that the models fitted the data well with relative high variance explained by the models, implying the hypothesized constructs under discussion were highly relevant, and hence the application for social cognitive theory in this context was supported. Conclusion: This thesis highlights the applicability of social cognitive theory on chronic disease self-management in older adults at risk of hospital readmission. Further studies are recommended to validate and continue to extend the development of social cognitive theory on chronic disease self-management in older adults to improve their nutritional and functional status, and health-related quality of life.

A risk simulation framework for information infrastructure protection

Information communication and technology (ICT) systems are almost ubiquitous in the modern world. It is hard to identify any industry, or for that matter any part of society, that is not in some way dependent on these systems and their continued secure operation. Therefore the security of information infrastructures, both on an organisational and societal level, is of critical importance. Information security risk assessment is an essential part of ensuring that these systems are appropriately protected and positioned to deal with a rapidly changing threat environment. The complexity of these systems and their inter-dependencies however, introduces a similar complexity to the information security risk assessment task. This complexity suggests that information security risk assessment cannot, optimally, be undertaken manually. Information security risk assessment for individual components of the information infrastructure can be aided by the use of a software tool, a type of simulation, which concentrates on modelling failure rather than normal operational simulation. Avoiding the modelling of the operational system will once again reduce the level of complexity of the assessment task. The use of such a tool provides the opportunity to reuse information in many different ways by developing a repository of relevant information to aid in both risk assessment and management and governance and compliance activities. Widespread use of such a tool allows the opportunity for the risk models developed for individual information infrastructure components to be connected in order to develop a model of information security exposures across the entire information infrastructure. In this thesis conceptual and practical aspects of risk and its underlying epistemology are analysed to produce a model suitable for application to information security risk assessment. Based on this work prototype software has been developed to explore these concepts for information security risk assessment. Initial work has been carried out to investigate the use of this software for information security compliance and governance activities. Finally, an initial concept for extending the use of this approach across an information infrastructure is presented.

Mother-child communication about sexual abuse prevention

Two hundred and twelve Australian mothers completed an online survey examining features of mother-child communication about child sexual abuse prevention. Two-thirds (67.5%) of respondents had discussed child sexual abuse prevention with their children. Proportions of mothers talking with their children about child sexual abuse prevention varied according to age range (highest for mothers with children aged 5-12 years) and only child status (lowest for mothers of only children). The number of topics discussed with their children differed according to child gender (greater number of topics discussed by mothers with both girls and boys) and age range (greater number of topics discussed by mothers with children aged 5-12 years). These findings provide new insights into mother-child communication about child sexual abuse prevention.

Theoretical perspectives on motorcycle training: reducing risk taking

Little past empirical support has been found for the efficacy of motorcycle rider training as a road safety countermeasure. However, it has been argued that rider training should focus more particularly on the psychosocial factors that influence risk taking behaviour in addition to the traditional practice of developing vehicle-handling skills. This paper examines how rider training to reduce risk taking could be guided by appropriate theories. Two fundamental perspectives are examined: firstly training can be considered in terms of behaviour change, and secondly in terms of adult learning. Whilst behaviour change theories assume some pre-existing level of dysfunctional behaviour, an adult learning perspective does not necessarily carry this assumption. This distinction in perspectives conceptually aligns with the notions of intervention and prevention (respectively), with possible implications for specific target groups for pre-licence and post-licence training. The application of the Theory of Reasoned Action (Ajzen & Fishbein, 1975, 1980) and Transformative Learning Theory (Mezirow, 1997) to a pre-licence rider training program in Queensland, Australia is discussed.

Measuring the effect of motorcycle rider training on psychosocial influences for risk taking

Aim: Whilst motorcycle rider training is commonly incorporated into licensing programs in many developed nations, little empirical support has been found in previous research to prescribe it as an effective road safety countermeasure. It has been posited that the lack of effect of motorcycle rider training on crash reduction may, in part, be due to the predominant focus on skills-based training with little attention devoted to addressing attitudes and motives that influence subsequent risky riding. However, little past research has actually endeavoured to measure attitudinal and motivational factors as a function of rider training. Accordingly, this study was undertaken to assess the effect of a commercial motorcycle rider training program on psychosocial factors that have been shown to influence risk taking by motorcyclists. Method: Four hundred and thirty-eight motorcycle riders attending a competency-based licence training course in Brisbane, Australia, voluntarily participated in the study. A self-report questionnaire adapted from the Rider Risk Assessment Measure (RRAM) was administered to participants at the commencement of training, then again at the conclusion of training. Participants were informed of the independent nature of the research and that their responses would in no way effect their chance of obtaining a licence. To minimise potential demand characteristics, participants were instructed to seal completed questionnaires in envelopes and place them in a sealed box accessible only by the research team (i.e. not able to be viewed by instructors). Results: Significant reductions in the propensity for thrill seeking and intentions to engage in risky riding in the next 12 months were found at the end of training. In addition, a significant increase in attitudes to safety was found. Conclusions: These findings indicate that rider training may have a positive short-term influence on riders’ propensity for risk taking. However, such findings must be interpreted with caution in regard to the subsequent safety of riders as these factors may be subject to further influence once riders are licensed and actively engage with peers during on-road riding. This highlights a challenge for road safety education / training programs in regard to the adoption of safety practices and the need for behavioural follow-up over time to ascertain long-term effects. This study was the initial phase of an ongoing program of research into rider training and risk taking framed around Theory of Planned Behaviour concepts. A subsequent 12 month follow-up of the study participants has been undertaken with data analysis pending.

Effects of impulsive sensation seeking, aggression and risk-taking behavior on the vulnerability of motorcyclists

While extensive research efforts have been devoted to improve the motorcycle safety, the relationship between the rider behavior and the crash risk is still not well understood.The objective of this study is to evaluate how behavioral factors influence crash risk and to identify the most vulnerable group of motorcyclists. To explore the rider behavior, a questionnaire containing 61-items of impulsive sensation seeking, aggression, and risk-taking behavior was developed. By clustering the crash risk using the medoid portioning algorithm, the log-linear model relating the rider behavior to crash risk has been developed. Results show that crash-involved motorcyclists score higher in all three behavioral traits. Aggressive and high risk-taking motorcyclists are more likely to fall under the high vulnerable group while impulsive sensation seeking is not found to be significant. Defining personality types from aggression and risk-taking behavior, “Extrovert” and “Follower” personality type of motorcyclists are more prone to crashes. The findings of this study will be useful for road safety campaign planners to be more focused in the target group as well as those who employ motorcyclists for their delivery business

PLDA based speaker recognition on short utterances

This paper investigates the effects of limited speech data in the context of speaker verification using a probabilistic linear discriminant analysis (PLDA) approach. Being able to reduce the length of required speech data is important to the development of automatic speaker verification system in real world applications. When sufficient speech is available, previous research has shown that heavy-tailed PLDA (HTPLDA) modeling of speakers in the i-vector space provides state-of-the-art performance, however, the robustness of HTPLDA to the limited speech resources in development, enrolment and verification is an important issue that has not yet been investigated. In this paper, we analyze the speaker verification performance with regards to the duration of utterances used for both speaker evaluation (enrolment and verification) and score normalization and PLDA modeling during development. Two different approaches to total-variability representation are analyzed within the PLDA approach to show improved performance in short-utterance mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development. The results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset suggest that the HTPLDA system can continue to achieve better performance than Gaussian PLDA (GPLDA) as evaluation utterance lengths are decreased. We also highlight the importance of matching durations for score normalization and PLDA modeling to the expected evaluation conditions. Finally, we found that a pooled total-variability approach to PLDA modeling can achieve better performance than the traditional concatenated total-variability approach for short utterances in mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development.

Navigational safety in port waters: Techniques for modeling collision risk

Navigational collisions are one of the major safety concerns for many seaports. Continuing growth of shipping traffic in number and sizes is likely to result in increased number of traffic movements, which consequently could result higher risk of collisions in these restricted waters. This continually increasing safety concern warrants a comprehensive technique for modeling collision risk in port waters, particularly for modeling the probability of collision events and the associated consequences (i.e., injuries and fatalities). A number of techniques have been utilized for modeling the risk qualitatively, semi-quantitatively and quantitatively. These traditional techniques mostly rely on historical collision data, often in conjunction with expert judgments. However, these techniques are hampered by several shortcomings, such as randomness and rarity of collision occurrence leading to obtaining insufficient number of collision counts for a sound statistical analysis, insufficiency in explaining collision causation, and reactive approach to safety. A promising alternative approach that overcomes these shortcomings is the navigational traffic conflict technique (NTCT), which uses traffic conflicts as an alternative to the collisions for modeling the probability of collision events quantitatively. This article explores the existing techniques for modeling collision risk in port waters. In particular, it identifies the advantages and limitations of the traditional techniques and highlights the potentials of the NTCT in overcoming the limitations. In view of the principles of the NTCT, a structured method for managing collision risk is proposed. This risk management method allows safety analysts to diagnose safety deficiencies in a proactive manner, which consequently has great potential for managing collision risk in a fast, reliable and efficient manner.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.

A toolbox for animal call recognition

Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems.

Tooth fracture risk analysis based on a new finite element dental structure models using micro-CT data

The finite element (FE) analysis is an effective method to study the strength and predict the fracture risk of endodontically-treated teeth. This paper presents a rapid method developed to generate a comprehensive tooth FE model using data retrieved from micro-computed tomography (μCT). With this method, the inhomogeneity of material properties of teeth was included into the model without dividing the tooth model into different regions. The material properties of the tooth were assumed to be related to the mineral density. The fracture risk at different tooth portions was assessed for root canal treatments. The micro-CT images of a tooth were processed by a Matlab software programme and the CT numbers were retrieved. The tooth contours were obtained with thresholding segmentation using Amira. The inner and outer surfaces of the tooth were imported into Solidworks and a three-dimensional (3D) tooth model was constructed. An assembly of the tooth model with the periodontal ligament (PDL) layer and surrounding bone was imported into ABAQUS. The material properties of the tooth were calculated from the retrieved CT numbers via ABAQUS user's subroutines. Three root canal geometries (original and two enlargements) were investigated. The proposed method in this study can generate detailed 3D finite element models of a tooth with different root canal enlargements and filling materials, and would be very useful for the assessment of the fracture risk at different tooth portions after root canal treatments.