Lift Yourself Up: The Short-Term Associations Between Strength Training and Mood States and the Longer Term Development of Physical Capital and "Grit" Among People Recovering From Substance Use Disorders.

Mind Body Connect is a charity that delivers community-based physical exercise (PE) classes to people in recovery from substance use disorders. This study aimed to identify the contribution that strength training played in the development of positive short- and long-term psychological attributes for people recovering from substance use disorders by assessing (a) the short-term associations between participation in group exercise classes (N = 43, t = 2,940) and (b) the prospective long-term association between strength development and individuals' self-assessed level of "grit" among participants for whom strength training became a habitual activity (N = 13, median 8 weeks supported training). Participants completed demographic, Profile of Mood States-Adapted Short Form, and Short Grit Scale questionnaires before and after each strength training session over a period of 45 noncompulsory training sessions between February 2020 and March 2021. Weekly training logs recording the weight lifted were normalized into a measure of physical capital development. Short-term associations were tested using multilevel regression models predicting change in mean Grit score with interactions for gender; long-term associations were assessed through the use of latent growth models. We found that training sessions were associated with weak to moderate reductions (ß = -0.112 to -0.533) in many negative mood states but elevated fatigue (ßMEN = 1.071, ßWOMEN = 0.397). Latent growth in recorded strength was strongly associated with latent growth in self-assessed grit (ß = 0.674). The findings suggest that PE classes and supported long-term strength training may assist with the development of recovery capital.

Inequalities in out-of-home care rates in England: Does local party politics matter?

BACKGROUND: Inequalities in the proportion of children experiencing abuse and neglect or a children's social care intervention have become a research focus in the last decade. One almost unexplored factor of growth in rates of children in out-of-home care is local party politics. OBJECTIVE: We assessed whether growth in rates of out-of-home care in England varied by local authority party political control. METHODS: We collated administrative data on the 152 local authorities in England between 2015 and 2021. We used Bayesian parallel process latent growth models to assess whether growth in rates of children in care in English local authorities has been equal across Labour, Conservative, No Overall Control, and no political majority councils before and after adjusting for trends in child poverty, household income from employment, and expenditure on preventative services. RESULTS: Prior to adjusting for trends in child poverty, average household income, and expenditure, we find little evidence for differences in trends; once adjusted, we find that trends in Labour authorities were lower (-1.125 children looked after per 10,000 per year) than in Conservative authorities. Had growth in family and local authority economic factors remained constant, our findings suggested out-of-home care rates would have, on average, been stable or decreasing in Labour local authorities but would have continued to rise elsewhere. CONCLUSIONS: The contribution of local party politics to growing rates of out-of-home care remains under-theorised and under-researched, but there are potentially substantial differences along party lines that are masked by unequal exposure to changes in poverty.

High-throughput and automated screening for COVID-19.

The COVID-19 pandemic has become a global challenge for the healthcare systems of many countries with 6 million people having lost their lives and 530 million more having tested positive for the virus. Robust testing and a comprehensive track and trace process for positive patients are essential for effective pandemic control, leading to high demand for diagnostic testing. In order to comply with demand and increase testing capacity worldwide, automated workflows have come into prominence as they enable high-throughput screening, faster processing, exclusion of human error, repeatability, reproducibility and diagnostic precision. The gold standard for COVID-19 testing so far has been RT-qPCR, however, different SARS-CoV-2 testing methods have been developed to be combined with high throughput testing to improve diagnosis. Case studies in China, Spain and the United Kingdom have been reviewed and automation has been proven to be promising for mass testing. Free and Open Source scientific and medical Hardware (FOSH) plays a vital role in this matter but there are some challenges to be overcome before automation can be fully implemented. This review discusses the importance of automated high-throughput testing, the different equipment available, the bottlenecks of its implementation and key selected case studies that due to their high effectiveness are already in use in hospitals and research centres.

Improving Inclusivity in Robotics Design: An Exploration of Methods for Upstream Co-Creation.

Disabled people are often involved in robotics research as potential users of technologies which address specific needs. However, their more generalised lived expertise is not usually included when planning the overall design trajectory of robots for health and social care purposes. This risks losing valuable insight into the lived experience of disabled people, and impinges on their right to be involved in the shaping of their future care. This project draws upon the expertise of an interdisciplinary team to explore methodologies for involving people with disabilities in the early design of care robots in a way that enables incorporation of their broader values, experiences and expectations. We developed a comparative set of focus group workshops using Community Philosophy, LEGO® Serious Play® and Design Thinking to explore how people with a range of different physical impairments used these techniques to envision a "useful robot". The outputs were then workshopped with a group of roboticists and designers to explore how they interacted with the thematic map produced. Through this process, we aimed to understand how people living with disability think robots might improve their lives and consider new ways of bringing the fullness of lived experience into earlier stages of robot design. Secondary aims were to assess whether and how co-creative methodologies might produce actionable information for designers (or why not), and to deepen the exchange of social scientific and technical knowledge about feasible trajectories for robotics in health-social care. Our analysis indicated that using these methods in a sequential process of workshops with disabled people and incorporating engineers and other stakeholders at the Design Thinking stage could potentially produce technologically actionable results to inform follow-on proposals.

Income inequality and child welfare interventions in England and Wales.

BACKGROUND: Previous research has identified a relationship between income inequality and child abuse and neglect in the USA. This association has received limited exploration outside the USA. METHODS: Administrative data on child protection (CP) in 172 English and Welsh local authorities between 2013 and 2018 were combined with data on deprivation, ethnic density and education from publicly available data sources. Commercial income data were used for Gini coefficient estimation. We tested whether similar evidence for three key findings from a US study could be found in England and Wales. These included whether there was evidence of a relationship between income inequality and child maltreatment, whether this relationship was non-linear and whether this relationship varied dependent on the level of poverty. RESULTS: There was a significant non-linear relationship between income inequality and state care rates in England and Wales. Predicted state care rates were higher as income inequality increased, up until around average levels where the effect flattens. However, there was no significant relationship for models predicting CP plan/register rates. Income inequality, income deprivation, ethnic density and higher education were able to explain around 75% of the variance in English and Welsh state care rates. CONCLUSIONS: There is some evidence to support the claim of a relationship between income inequality and child maltreatment beyond the USA in England and Wales, and a case for further comparative research, but there are significant limitations in the comparability of data.