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There is a growing recognition that needs more to be done to ensure that research contributes to better health services and patient outcomes. Stakeholder engagement in research, including co-production, has been identified as a promising mechanism for improving the value, relevance and utilization of research. This article presents findings from a prospective study which explored the impact of stakeholder engagement in a 3-year European tobacco control research project. That research project aimed to engage stakeholders in the development, testing and dissemination of a return-on-investment tool across five EU countries (the Netherlands, Spain, Hungary, Germany and the UK). The prospective study comprised interviews, observations and document review. The analysis focused on the extent to which the project team recognized, conceptualized and operationalized stakeholder engagement over the course of the research project. Stakeholder engagement in the European research project was conceptualized as a key feature of pre-designated spaces within their work programme. Over the course of the project, however, the tool development work and stakeholder engagement activities decoupled. While the modelling and tool development became more secluded, stakeholder engagement activities subtly transformed from co-production, to consultation, to something more recognizable as research participation. The contribution of this article is not to argue against the potential contribution of stakeholder engagement and co-production, but to show how even well-planned engagement activities can be diverted within the existing research funding and research production systems where non-research stakeholders remain at the margins and can even be seen as a threat to academic identify and autonomy.

This paper will discuss research undertaken within a London borough in 2009 that aimed to examine how Personal, Social, Health and Economic education (PSHE) was perceived and delivered. The ethos of schools was incorporated into the enquiry as a key determinate of both perception and delivery of PSHE. The findings are presented with particular reference to three areas: school ethos, participation and evaluation. The overall conclusion of the study is that staff value PSHE, but with some reservations. Staff believe that PSHE has a positive impact on pupil behaviour. The value placed by schools on PSHE, and how it interrelates with the existing ethos of schools, remains a crucial factor in its provision. (Contains 8 notes.)

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575 h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data. [ABSTRACT FROM AUTHOR]

Aim This paper aims to explore patient and public representation in a NHS clinical commissioning group and how this is experienced by staff and lay members involved. Background Patient and public involvement is believed to foster greater public representativeness in the development and delivery of health care services. However, there is widespread debate about what representation is or what it should be. Questions arise about the different constructions of representation and the representativeness of patients and the public in decision-making structures and processes. Design Ethnographic, two-phase study involving twenty-four observations across two types of clinical commissioning group meetings with patient and public involvement, fourteen follow-up interviews with NHS staff and lay members, and a focus group with five lay members. Results Perceptions of what constitutes legitimate representativeness varied between respondents, ranging from representing an individual patient experience to reaching large numbers of people. Consistent with previous studies, there was a lack of clarity about the role of lay members in the work of the clinical commissioning group. Conclusions Unlike previous studies, it was lay members, not staff, who raised concerns about their representativeness and legitimacy. Although the clinical commissioning group provides resources to support patient and public involvement, there continues to be a lack of clarity about roles and scope for impact. Lay members are still some way from constituting a powerful voice at the table. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of implementation of an analysis framework of neonatal EEG, including ML, sonification and intuitive visualization, on a low power IoT edge device. Electroencephalography (EEG) analysis is a very important tool to detect brain disorders. Neonatal seizure detection is a known, challenging problem. Under-resourced communities across the globe are particularly affected by the cost associated with EEG analysis and interpretation. Machine learning (ML) techniques have been successfully utilized to automate seizure detection in neonatal EEG, in order to assist a healthcare professional in visual analysis. Several usage scenarios are reviewed in this study. It is shown that both sonification and ML can be efficiently implemented on low-power edge platforms without any loss of accuracy. The developed platform can be easily expanded to address EEG analysis applications in neonatal and adult population.

Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work proposes a deep neural network architecture which is capable of extracting information from long segments of EEG. Residual connections as well as data augmentation and a more robust optimizer are efficiently exploited to train a deeper architecture with an increased receptive field and longer EEG input. The proposed system is tested on a large clinical dataset of 4,570 hours of duration and benchmarked on a publicly available Helsinki dataset of 112 hours duration. The performance has improved from an AUC of 95.41% to an AUC of 97.73% when compared to a deep learning baseline.

This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.