Young people's experiences of COVID-19 messaging at the start of the UK lockdown: lessons for positive engagement and information sharing.

BACKGROUND: To reduce COVID-19 infection rates during the initial stages of the pandemic, the UK Government mandated a strict period of restriction on freedom of movement or 'lockdown'. For young people, closure of schools and higher education institutions and social distancing rules may have been particularly challenging, coming at a critical time in their lives for social and emotional development. This study explored young people's experiences of the UK Government's initial response to the pandemic and related government messaging. METHODS: This qualitative study combines data from research groups at the University of Southampton, University of Edinburgh and University College London. Thirty-six online focus group discussions (FGDs) were conducted with 150 young people (Southampton: n = 69; FGD = 7; Edinburgh: n = 41; FGD = 5; UCL: n = 40; FGD = 24). Thematic analysis was conducted to explore how young people viewed the government's response and messaging and to develop recommendations for how to best involve young people in addressing similar crises in the future. RESULTS: The abrupt onset of lockdown left young people shocked, confused and feeling ignored by government and media messaging. Despite this, they were motivated to adhere to government advice by the hope that life might soon return to normal. They felt a responsibility to help with the pandemic response, and wanted to be productive with their time, but saw few opportunities to volunteer. CONCLUSIONS: Young people want to be listened to and feel they have a part to play in responding to a national crisis such as the COVID-19 epidemic. To reduce the likelihood of disenfranchising the next generation, Government and the media should focus on developing messaging that reflects young people's values and concerns and to provide opportunities for young people to become involved in responses to future crises.

Exploring experiences and impact of the COVID-19 pandemic on young racially minoritised people in the United Kingdom: A qualitative study.

Within high-income-countries, the COVID-19 pandemic has disproportionately impacted people from racially minoritised backgrounds. There has been significant research interrogating the disparate impact of the virus, and recently, interest in the long-term implications of the global crisis on young people's mental health and wellbeing. However, less work explores the experiences of young people from racialised backgrounds as they navigate the pandemic, and the specific consequences this has for their mental health. Forty young people (age 16-25) from Black, mixed and other minority backgrounds and living in London, participated in consecutive focus group discussions over a two-month period, to explore the impact of the pandemic on their lives and emotional wellbeing. Thematic analysis identified seven thematic categories describing the impact of the pandemic, indicating: deepening of existing socioeconomic and emotional challenges; efforts to navigate racism and difference within the response; and survival strategies drawing on communal and individual resources. Young people also articulated visions for a future public health response which addressed gaps in current strategies. Findings point to the need to contextualize public health responses to the pandemic in line with the lived experiences of racialised young people. We specifically note the importance of long-term culturally and socio-politically relevant support interventions. Implications for policy and practice are discussed.

Human-in-the-Loop Predictive Analytics Using Statistical Learning.

The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical research field, currently, the prediction of these diseases is performed only using the data gathered from the devices only; however, the human's input is much essential to accurately understand their health condition to take appropriate decision on time. Therefore, we have proposed a healthcare framework involving the concept of artificial intelligence in the human-in- the-loop cyber-physical system. This model works via a response loop in which the human's intention is concluded by gathering biological signals and context data, and then, the decision is interpreted to a system action that is recognizable to the human in the physical environment, thereby completing the loop. In this paper, we have designed a model for early prognosis of coma using the electroencephalogram dataset. In the proposed approach, we have achieved the best results using a statistical learning algorithm called autoregressive integrated moving average in comparison to artificial neural networks and long short-term memory models. In order to measure the efficiency of our model, we have used the root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE) value to evaluate the linear models as it gives the difference between the measured value and true or correct value. We have achieved the least possible error value for our dataset. To conduct this experiment, we used the dataset available in the phsyionet opensource community.

A robust approach for industrial small-object detection using an improved faster regional convolutional neural network.

With the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution to detect and improve potential obligations as soon as possible. The systems working in industrial environments for generating industrial products are very fast and generate products rapidly, sometimes leading to faulty products. Therefore, this problem needs to be solved efficiently. Considering this problem in terms of faulty small-object detection, this study proposed an improved faster regional convolutional neural network-based model to detect the faults in the product images. We introduced a novel data-augmentation method along with a bi-cubic interpolation-based feature amplification method. A center loss is also introduced in the loss function to decrease the inter-class similarity issue. The experimental results show that the proposed improved model achieved better classification accuracy for detecting our small faulty objects. The proposed model performs better than the state-of-the-art methods.

Evaluating case studies of community-oriented integrated care.

This paper summarises a ten-year conversation within London Journal of Primary Care about the nature of community-oriented integrated care (COIC) and how to develop and evaluate it. COIC means integration of efforts for combined disease-treatment and health-enhancement at local, community level. COIC is similar to the World Health Organisation concept of a Community-Based Coordinating Hub - both require a local geographic area where different organisations align their activities for whole system integration and develop local communities for health. COIC is a necessary part of an integrated system for health and care because it enables multiple insights into 'wicked problems', and multiple services to integrate their activities for people with complex conditions, at the same time helping everyone to collaborate for the health of the local population. The conversation concludes seven aspects of COIC that warrant further attention.