Exploring recruitment, willingness to participate, and retention of low-SES women in stress and depression prevention

Contains fulltext : 90907.pdf (publisher's version ) (Open Access)Background Recruitment, willingness to participate, and retention in interventions are indispensable for successful prevention. This study investigated the effectiveness of different strategies for recruiting and retaining low-SES women in depression prevention, and explored which sociodemographic characteristics and risk status factors within this specific target group are associated with successful recruitment and retention. Methods The process of recruitment, willingness to participate, and retention was structurally mapped and explored. Differences between women who dropped out and those who adhered to the subsequent stages of the recruitment and retention process were investigated. The potential of several referral strategies was also studied, with specific attention paid to the use of GP databases. Results As part of the recruitment process, 12.1% of the target population completed a telephone screening. The most successful referral strategy was the use of patient databases from GPs working in disadvantaged neighborhoods. Older age and more severe complaints were particularly associated with greater willingness to participate and with retention. Conclusions Low-SES women can be recruited and retained in public health interventions through tailored strategies. The integration of mental health screening within primary care might help to embed preventive interventions in low-SES communities.8 p

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Women's Preferences for Treatment of Perinatal Depression and Anxiety : A Discrete Choice Experiment

Perinatal depression and anxiety (PNDA) are an international healthcare priority, associated with significant short- and long-term problems for women, their children and families. Effective treatment is available but uptake is suboptimal: some women go untreated whilst others choose treatments without strong evidence of efficacy. Better understanding of women's preferences for treatment is needed to facilitate uptake of effective treatment. To address this issue, a discrete choice experiment (DCE) was administered to 217 pregnant or postnatal women in Australia, who were recruited through an online research company and had similar sociodemographic characteristics to Australian data for perinatal women. The DCE investigated preferences regarding cost, treatment type, availability of childcare, modality and efficacy. Data were analysed using logit-based models accounting for preference and scale heterogeneity. Predicted probability analysis was used to explore relative attribute importance and policy change scenarios, including how these differed by women's sociodemographic characteristics. Cost and treatment type had the greatest impact on choice, such that a policy of subsidising effective treatments was predicted to double their uptake compared with the base case. There were differences in predicted uptake associated with certain sociodemographic characteristics: for example, women with higher educational attainment were more likely to choose effective treatment. The findings suggest policy directions for decision makers whose goal is to reduce the burden of PNDA on women, their children and families

Development and psychometric properties the Barriers to Access to Care Evaluation scale (BACE) related to people with mental ill health

Many people with mental illness do not seek or delay seeking care. This study aimed to develop, and provide an initial validation of, a comprehensive measure for assessing barriers to access to mental health care including a 'treatment stigma' subscale, and to present preliminary evidence about the prevalence of barriers experienced by adults currently or recently using secondary mental health services in the UK

Human Factors Evaluation of Conflict Resolution Advisories in the En Route Domain

Objective: In this human-in-the-loop simulation experiment, we evaluated how Conflict Resolution Advisories (CRA) affected en route controllers. Background: Controllers currently use a conflict probe and trial planning tool, known as the User Request Evaluation Tool (URET), which is available on the Radar Associate Position. However, under Trajectory-Based Operations\u2014that is, Separation Management Modern Procedures (SepMan)\u2014several capabilities will become available to the Radar Position, including probed menus, conflict detection and trial planning, and support for multiple separation minima within a sector\u2018s airspace. The CRA Program is built upon the SepMan concept and will provide a proposed solution to a potential conflict as soon as a controller initiates the entry of a clearance. Method: Twelve current en route Certified Professional Controllers from Air Route Traffic Control Centers participated in the experiment. Results: CRA capabilities did not change controller workload nor time and distance flown by aircraft in the sector. Analysis of tactical and strategic conflict alerts show that controllers solved potential conflicts quickly when CRA was available. Most of the participants\u2018 subjective ratings favored the CRA, and participants expressed that CRA was a useful concept. Conclusion: The results show an advantage of CRA on some air traffic control tasks. In general, CRA was accepted by the participant controllers. Application: With a few modifications of the current CRA features and functions, the authors believe that CRA will be a useful automation tool for air traffic controllers

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks

This work proposes and develops an implementation of a fault location method to provide a fast and resilient protection scheme for power distribution systems. The method analyzes the transient dynamics of traveling waves (TWs) to generate features using the discrete wavelet transform (DWT), which are then used to train several graph convolutional network (GCN) models. Faults are simulated in the IEEE 34-node system, which is divided into three protection zones (PZs). The goal is to identify the PZ in which the fault occurs. The GCN models create a distributed protection scheme, as all nodes are able to retrieve a prediction. Given that message-passing between nodes occurs both during training and in the execution of the model, the resiliency of such schemes to communication losses was analyzed and demonstrated. One of the models, which only uses voltage measurements, was implemented on a Texas Instruments F28379D development board. The execution times were monitored to assess the speed of the protection scheme. It is shown that the proposed method can be executed in approximately a millisecond, which is comparable to existing TW protection in the transmission system. For experimental purposes, a DWT-based detection method is employed. A design of a setup to playback TWs using two development boards is also addressed

Embedded, Real-Time, and Distributed Traveling Wave Fault Location Method Using Graph Convolutional Neural Networks

This work proposes and develops an implementation of a fault location method to provide a fast and resilient protection scheme for power distribution systems. The method analyzes the transient dynamics of traveling waves (TWs) to generate features using the discrete wavelet transform (DWT), which are then used to train several graph convolutional network (GCN) models. Faults are simulated in the IEEE 34-node system, which is divided into three protection zones (PZs). The goal is to identify the PZ in which the fault occurs. The GCN models create a distributed protection scheme, as all nodes are able to retrieve a prediction. Given that message-passing between nodes occurs both during training and in the execution of the model, the resiliency of such schemes to communication losses was analyzed and demonstrated. One of the models, which only uses voltage measurements, was implemented on a Texas Instruments F28379D development board. The execution times were monitored to assess the speed of the protection scheme. It is shown that the proposed method can be executed in approximately a millisecond, which is comparable to existing TW protection in the transmission system. For experimental purposes, a DWT-based detection method is employed. A design of a setup to playback TWs using two development boards is also addressed