Football in the community schemes: Exploring the effectiveness of an intervention in promoting healthful behaviour change

This study aims to examine the effectiveness of a Premier League football club’s Football in the Community (FitC) schemes intervention in promoting positive healthful behaviour change in children. Specifically, exploring the effectiveness of this intervention from the perspectives of the participants involved (i.e. the researcher, teachers, children and coaches). A range of data collection techniques were utilized including the principles of ethnography (i.e. immersion, engagement and observations), alongside conducting focus groups with the children. The results allude to the intervention merely ‘keeping active children active’ via (mostly) fun, football sessions. Results highlight the important contribution the ‘coach’ plays in the effectiveness of the intervention. Results relating to working practice (i.e. coaching practice and coach recruitment) are discussed and highlighted as areas to be addressed. FitC schemes appear to require a process of positive organizational change to increase their effectiveness in strategically attending to the health agenda

The Development of Language Learning Strategies

This article discusses the strategy repertoires and strategy development of six English children who learned foreign languages at primary school. My study differs from mainstream research in that it focuses on young children and on the development of their strategies, draws on sociocultural theory and uses ethnographic methods. My findings show that the six children developed a range of strategies over the course of a calendar year in spite of receiving no direct strategy instruction. The primary classroom encouraged learner autonomy and stimulated children to reflect on their learning which, in turn, enabled them to refine their strategies

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

A Bayesian Optimisation Workflow for Field Development Planning Under Geological Uncertainty

Field development planning using reservoir models is a key step in the field development process. Numerical optimisation of specific field development strategies is often used to aid planning. Bayesian Optimisation is a popular optimisation method that has previously been applied to this problem. However, reservoir models can have a high degree of geological uncertainty associated with them, even after history matching. It is important to be able to perform optimisation that accounts for this uncertainty. To date, limited attention has been given to Bayesian Optimisation of field development strategies under geological uncertainty. Much of the recent work in this area has focused on Ensemble Optimisation methods. These naturally handle geological uncertainty using ensembles of geological realisations. This can result in a high computational cost, as large ensembles are required to capture the geological uncertainty. Bayesian Optimisation offers an alternative solution using probabilistic surrogate or proxy models that can capture the geological uncertainty. However, incorporating geological uncertainty into proxy models and using those models in a Bayesian Optimisation loop remains a challenging task. Further, the effect of the additional proxy model uncertainty on optimisation results has not been well studied. We propose a Bayesian Optimisation workflow comprising a Stochastic Bayes Linear proxy model and a combination of experimental and sequential design techniques. The workflow is designed to include a combination of static and dynamic uncertainties, with a new geological realisation generated and used to simulate fluid flow during each run of the model. The workflow is demonstrated by optimising several field development strategies in a synthetic North Sea reservoir model. The ability of the workflow to locate optima and correctly account for the geological uncertainty is studied and the computational cost is quantified. The performance and practical implications of the proposed approach are discussed. These are important in designing an accurate and computationally efficient optimisation workflow under geological uncertainty and, ultimately, are factors in developing decision support tools for field development