Matthew Browne

Understanding gambling related harm: a proposed definition, conceptual framework, and taxonomy of harms

BackgroundHarm from gambling is known to impact individuals, families, and communities; and these harms are not restricted to people with a gambling disorder. Currently, there is no robust and inclusive internationally agreed upon definition of gambling harm. In addition, the current landscape of gambling policy and research uses inadequate proxy measures of harm, such as problem gambling symptomology, that contribute to a limited understanding of gambling harms. These issues impede efforts to address gambling from a public health perspective.MethodsData regarding harms from gambling was gathered using four separate methodologies, a literature review, focus groups and interviews with professionals involved in the support and treatment of gambling problems, interviews with people who gamble and their affected others, and an analysis of public forum posts for people experiencing problems with gambling and their affected others. The experience of harm related to gambling was examined to generate a conceptual framework. The catalogue of harms experienced were organised as a taxonomy.ResultsThe current paper proposes a definition and conceptual framework of gambling related harm that captures the full breadth of harms that gambling can contribute to; as well as a taxonomy of harms to facilitate the development of more appropriate measures of harm.ConclusionsOur aim is to create a dialogue that will lead to a more coherent interpretation of gambling harm across treatment providers, policy makers and researchers.

A multiscale polynomial filter for adaptive smoothing

The effectiveness of Savitzky-Golay type symmetric polynomial smoothers is known to be strongly dependent on the window size. Many authors note that selection of the appropriate window size is essential for achieving the correct trade-off between noise reduction and avoiding the introduction of bias. However, it is often overlooked that, in the case of non-stationary signals, the optimal window size will vary with the dynamics of the signal. A multiresolution approach is outlined, along with criteria for varying window size with respect to translation, based on evaluation of the residuals of the smoothed data in the local region. Adaptive window polynomial smoothing is shown to be superior to fixed window smoothing for a test signal at various signal-to-noise ratios.

Low-probability event-detection and separation via statistical wavelet thresholding: an application to psychophysiological denoising.

OBJECTIVES The aim of this paper is to introduce and test a general, wavelet-based method for the automatic removal of noise and artefact from psychophysiological data. METHODS Statistical wavelet thresholding (SWT) performs blind source separation by transforming data to the wavelet domain, and subsequent filtering of wavelet coefficients based on a statistical framework. The observed wavelet coefficients are modelled using a Gaussian distribution, from which low-probability outliers are attenuated based on their z-scores. RESULTS The technique was applied to both simulated and real event-related potentials (ERP) data. SWT applied to artificial data displayed increased signal-to-noise ratio (SNR) improvements as noise amplitude increased. ERP averages of filtered experimental data displayed a correlation of 0.93 with operator-filtered data, compared with a correlation of 0.56 for unfiltered data. The energy of operator-designated contaminated trials was attenuated by a factor of 7.46 relative to uncontaminated trials. SNR improvement was observed in simulated tests. CONCLUSIONS Variations of SWT may be useful in situations where one wishes to separate uncommon/uncharacteristic structures from time series data sets. For artefact removal applications, SWT appears to be a valid alternative to expert operator screening.

Going against the Herd: Psychological and Cultural Factors Underlying the ‘Vaccination Confidence Gap’

By far the most common strategy used in the attempt to modify negative attitudes toward vaccination is to appeal to evidence-based reasoning. We argue, however, that focusing on science comprehension is inconsistent with one of the key facts of cognitive psychology: Humans are biased information processors and often engage in motivated reasoning. On this basis, we hypothesised that negative attitudes can be explained primarily by factors unrelated to the empirical evidence for vaccination; including some shared attitudes that also attract people to complementary and alternative medicine (CAM). In particular, we tested psychosocial factors associated with CAM endorsement in past research; including aspects of spirituality, intuitive (vs analytic) thinking styles, and the personality trait of openness to experience. These relationships were tested in a cross-sectional, stratified CATI survey (N = 1256, 624 Females). Whilst educational level and thinking style did not predict vaccination rejection, psychosocial factors including: preferring CAM to conventional medicine (OR .49, 95% CI .36–.66), endorsement of spirituality as a source of knowledge (OR .83, 95% CI .71–.96), and openness (OR .86, 95% CI .74–.99), all predicted negative attitudes to vaccination. Furthermore, for 9 of the 12 CAMs surveyed, utilisation in the last 12 months was associated with lower levels of vaccination endorsement. From this we suggest that vaccination scepticism appears to be the outcome of a particular cultural and psychological orientation leading to unwillingness to engage with the scientific evidence. Vaccination compliance might be increased either by building general confidence and understanding of evidence-based medicine, or by appealing to features usually associated with CAM, e.g. ‘strengthening your natural resistance to disease’.

Convolutional Neural Networks for Image Processing: An Application in Robot Vision

Convolutional neural networks (CNNs) represent an interesting method for adaptive image processing, and form a link between general feed-forward neural networks and adaptive filters. Two dimensional CNNs are formed by one or more layers of two dimensional filters, with possible non-linear activation functions and/or down-sampling. CNNs possess key properties of translation invariance and spatially local connections (receptive fields). We present a description of the convolutional network architecture, and an application to practical image processing on a mobile robot. A CNN is used to detect and characterize cracks on an autonomous sewer inspection robot. The filter sizes used in all cases were 4x4, with non-linear activations between each layer. The number of feature maps used in the three hidden layers was, from input to output, 4, 4, 4. The network was trained using a dataset of 48x48 sub-regions drawn from 30 still image 320x240 pixel frames sampled from a pre-recorded sewer pipe inspection video. 15 frames were used for training and 15 for validation of network performance. Although development of a CNN system for civil use is on-going, the results support the notion that data-based adaptive image processing methods such as CNNs are useful for image processing, or other applications where the input arrays are large, and spatially / temporally distributed. Further refinements of the CNN architecture, such as the implementation of separable filters, or extensions to three dimensional (ie. video) processing, are suggested.

A geometric approach to non-parametric density estimation

A novel non-parametric density estimator is developed based on geometric principles. A penalised centroidal Voronoi tessellation forms the basis of the estimator, which allows the data to self-organise in order to minimise estimate bias and variance. This approach is a marked departure from usual methods based on local averaging, and has the advantage of being naturally adaptive to local sample density (scale-invariance). The estimator does not require the introduction of a plug-in kernel, thus avoiding assumptions of symmetricity and morphology. A numerical experiment is conducted to illustrate the behaviour of the estimator, and its characteristics are discussed.

Echo State Networks and Self-Prediction

Prediction occurs in many biological nervous systems e.g. in the cortex [7]. We introduce a method of adapting the recurrent layer dynamics of an echo-state network (ESN) without attempting to train the weights directly. Initially a network is generated that fulfils the echo state – liquid state condition. A second network is then trained to predict the next internal state of the system. In simulation, the prediction of this module is then mixed with the actual activation of the internal state neurons, to produce dynamics that a partially driven by the network model, rather than the input data. The mixture is determined by a parameter α. The target function be produced by the network was sin 3(0.24t), given an input function sin(0.24t). White noise was added to the input signal at 15% of the amplitude of the signal. Preliminary results indicate that self prediction may improve performance of an ESN when performing signal mappings in the presence of additive noise.

Internalized and Externalized Continuing Bonds in Bereaved Parents: Their Relationship with Grief Intensity and Personal Growth

Continuing bonds (CBs) expression appears especially prevalent among bereaved parents. This study examined the relationship between CBs and grief outcomes for this population. A customized CB scale for use with bereaved parents was derived from the literature. Three hundred fifty-four participants (10 male) recruited from online support groups completed an internet questionnaire. A 3-factor dimensional structure of CB (internalized, externalized, and transference) was supported. Structural equation modeling showed clear links between internalized bonds and a more positive grief status; externalized bonds showing an opposite relationship. Weaker effects were found for childs age, time since death, and type of death.

Psychosocial factors that predict why people use complementary and alternative medicine and continue with its use: A population based study

UNLABELLED Studies have explored the predictors of CAM use but fewer data explain the psychosocial factors associated with this and why people continue with CAM. AIMS To examine the psychosocial factors that predict CAM use; to explore the predictors of continuing with CAM. DESIGN A cross sectional survey. METHODS 1256 adults were interviewed as part of 2012 Queensland Social Survey. We included questions about CAM, perceived control, cognitive style, spirituality and openness. Relationships were explored using bivariate and multiple logistic regression. RESULTS 79% of people had used CAM in the last 12 months. Socio-demographics, health behaviours, spirituality, openness and prescribing sources were the strongest predictors of CAM use. General health, chronic illness and prescribing sources predicted continued CAM use. CONCLUSION There was high CAM use in Queensland, Australia. Personal characteristics and psychosocial factors need to be considered as part of the individuals holistic assessment and on-going care.

Empirical Estimation of Nearshore Waves From a Global Deep-Water Wave Model

Global wind-wave models such as the National Oceanic and Atmospheric Administration WaveWatch 3 (NWW3) play an important role in monitoring the worlds oceans. However, untransformed data at grid points in deep water provide a poor estimate of swell characteristics at nearshore locations, which are often of significant scientific, engineering, and public interest. Explicit wave modeling, such as the Simulating Waves Nearshore (SWAN), is one method for resolving the complex wave transformations affected by bathymetry, winds, and other local factors. However, obtaining accurate bathymetry and determining parameters for such models is often difficult. When target data is available (i.e., from in situ buoys or human observers), empirical alternatives such as artificial neural networks (ANNs) and linear regression may be considered for inferring nearshore conditions from offshore model output. Using a sixfold cross-validation scheme, significant wave height Hs and period were estimated at one onshore and two nearshore locations. In estimating Hs at the shoreline, the validation performance of the best ANN was r=0.91, as compared to those of linear regression (0.82), SWAN (0.78), and the NWW3 Hs baseline (0.54)