Inadequate reporting of research ethics review and informed consent in cluster randomized trials : review of random sample of published trials

Peer reviewedPublisher PD

The impact of antenatal psychological group interventions on psychological well-being : a systematic review of the qualitative and quantitative evidence

Depression, anxiety and stress in the perinatal period can have serious, long-term consequences for women, their babies and their families. Over the last two decades, an increasing number of group interventions with a psychological approach have been developed to improve the psychological well-being of pregnant women. This systematic review examines interventions targeting women with elevated symptoms of, or at risk of developing, perinatal mental health problems, with the aim of understanding the successful and unsuccessful features of these interventions. We systematically searched online databases to retrieve qualitative and quantitative studies on psychological antenatal group interventions. A total number of 19 papers describing 15 studies were identified; these included interventions based on cognitive behavioural therapy, interpersonal therapy and mindfulness. Quantitative findings suggested beneficial effects in some studies, particularly for women with high baseline symptoms. However, overall there is insufficient quantitative evidence to make a general recommendation for antenatal group interventions. Qualitative findings suggest that women and their partners experience these interventions positively in terms of psychological wellbeing and providing reassurance of their ‘normality’. This review suggests that there are some benefits to attending group interventions, but further research is required to fully understand their successful and unsuccessful features

ShopSmart 4 Health - protocol of a skills-based randomised controlled trial promoting fruit and vegetable consumption among socioeconomically disadvantaged women

BackgroundThere is a need for evidence on the most effective and cost-effective approaches for promoting healthy eating among groups that do not meet dietary recommendations for good health, such as those with low incomes or experiencing socioeconomic disadvantage. This paper describes the ShopSmart 4 Health study, a randomised controlled trial conducted by Deakin University, Coles Supermarkets and the Heart Foundation, to investigate the effectiveness and cost-effectiveness of a skill-building intervention for promoting increased purchasing and consumption of fruits and vegetables amongst women of low socioeconomic position (SEP).Methods/designShopSmart 4 Health employed a randomised controlled trial design. Women aged 18&ndash;60 years, holding a Coles store loyalty card, who shopped at Coles stores within socioeconomically disadvantaged neighbourhoods and met low-income eligibility criteria were invited to participate. Consenting women completed a baseline survey assessing food shopping and eating habits and food-related behaviours and attitudes. On receipt of their completed survey, women were randomised to either a skill-building intervention or a wait-list control condition. Intervention effects will be evaluated via self-completion surveys and using supermarket transaction sales data, collected at pre- and post-intervention and 6-month follow-up. An economic evaluation from a societal perspective using a cost-consequences approach will compare the costs and outcomes between intervention and control groups. Process evaluation will be undertaken to identify perceived value and effects of intervention components.DiscussionThis study will provide data to address the currently limited evidence base regarding the effectiveness and cost-effectiveness of skill-building intervention strategies aimed at increasing fruit and vegetable consumption among socioeconomically disadvantaged women, a target group at high risk of poor diets.<br /

Supervised Human-Guided Data Exploration

Peer reviewe

Towards self-attention based visual navigation in the real world

Vision guided navigation requires processing complex visual information to inform task-orientated decisions. Applications include autonomous robots, self-driving cars, and assistive vision for humans. A key element is the extraction and selection of relevant features in pixel space upon which to base action choices, for which Machine Learning techniques are well suited. However, Deep Reinforcement Learning agents trained in simulation often exhibit unsatisfactory results when deployed in the real-world due to perceptual differences known as the $\textit{reality gap}$. An approach that is yet to be explored to bridge this gap is self-attention. In this paper we (1) perform a systematic exploration of the hyperparameter space for self-attention based navigation of 3D environments and qualitatively appraise behaviour observed from different hyperparameter sets, including their ability to generalise; (2) present strategies to improve the agents' generalisation abilities and navigation behaviour; and (3) show how models trained in simulation are capable of processing real world images meaningfully in real time. To our knowledge, this is the first demonstration of a self-attention based agent successfully trained in navigating a 3D action space, using less than 4000 parameters.Comment: Submitted to The 2022 Australian Conference on Robotics and Automation (ACRA 2022

Tightly-coupled manipulation pipelines: Combining traditional pipelines and end-to-end learning

Traditionally, robot manipulation tasks are solved by engineering solutions in a modular fashion --- typically consisting of object detection, pose estimation, grasp planning, motion planning, and finally run a control algorithm to execute the planned motion. This traditional approach to robot manipulation separates the hard problem of manipulation into several self-contained stages, which can be developed independently, and gives interpretable outputs at each stage of the pipeline. However, this approach comes with a plethora of issues, most notably, their generalisability to a broad range of tasks; it is common that as tasks get more difficult, the systems become increasingly complex. To combat the flaws of these systems, recent trends have seen robots visually learning to predict actions and grasp locations directly from sensor input in an end-to-end manner using deep neural networks, without the need to explicitly model the in-between modules. This thesis investigates a sample of methods, which fall somewhere on a spectrum from pipelined to fully end-to-end, which we believe to be more advantageous for developing a general manipulation system; one that could eventually be used in highly dynamic and unpredictable household environments. The investigation starts at the far end of the spectrum, where we explore learning an end-to-end controller in simulation and then transferring to the real world by employing domain randomisation, and finish on the other end, with a new pipeline, where the individual modules bear little resemblance to the "traditional" ones. The thesis concludes with a proposition of a new paradigm: Tightly-coupled Manipulation Pipelines (TMP). Rather than learning all modules implicitly in one large, end-to-end network or conversely, having individual, pre-defined modules that are developed independently, TMPs suggest taking the best of both world by tightly coupling actions to observations, whilst still maintaining structure via an undefined number of learned modules, which do not have to bear any resemblance to the modules seen in "traditional" systems.Open Acces