Empowering schools to identify fundamental movement skill difficulties: reducing inequalities and improving outcomes for children

获得运动技能是儿童发展的重要组成部分。儿童能够使用感官信息来指导他们的动作，这使得他们能够了解他们周围的世界。基本运动技能(FMS)是一组特殊的运动技能，它被认为是影响教育和幸福结果的风险因素。在布拉德福德出生的儿童(BIB)的研究表明，在贫困和少数民族社区中，有不成比例的高比例儿童有FMS问题，但不太可能获得此类困难的帮助。我的博士开发了一种基于学校的FMS通用筛查工具(FUNMOVES)，以减少不平等并加快识别将受益于额外教育支持的神经多样性儿童。这笔奖学金将使我能够扩大FUNMOVES的影响范围，增加儿童幸福健康生活的可能性。这笔奖学金也将对我的专业发展产生重大影响。该研究金范围内的工作将分为四大类：(I)开展进一步研究。数据将从布拉德福德10所小学(n=~3600)1-6年级的儿童中收集。这些数据将被用来开始了解FUNMOVES的哪些分数构成“正常发育”，这样就可以应用一个分界值来识别有其他需要的儿童。在我攻读博士学位期间，人们将孩子们与在校同年的其他孩子进行比较。通过收集全区小学的数据，发现的困难将更准确地反映一般人口。收集的这3600名儿童的数据也将被输入到互联的布拉德福德数据集中，从而将该地区所有儿童的教育和医疗记录联系起来。通过将FUNMOVES信息整合到这个数据集中，它将能够对儿童运动技能在教育和福祉不平等中所起的作用进行大规模评估。(Ii)分享我的发现。我将主持一次联合制作活动，向学校教职员工、政策制定者(例如教育部)和相关利益攸关方介绍FUNMOVES。在此期间，我会询问如何在现实世界中可行地扩大FUNMOVES的生活体验。我将介绍我在联合制作活动中的发现，以及计划在大型国际会议上进行的进一步研究，这些会议汇集了包括心理学和教育学在内的一系列学科的学者。我也会写这两篇文章，以便(I)在主要学术期刊上发表文章和(Ii)给教育部的简报信。(Iii)增强我的研究技能和合作网络。为了分析上述附加研究的数据，作为一名社会科学研究人员，我需要获得高级定量分析技能。借助布拉德福德大学计算机科学方面的专业知识，我会参加一些课程，以提高我的数据分析能力。我还将在斯坦福大学人口健康科学系进行一次考察访问，以提高我处理大规模数据集的能力，并学习该领域的最新技术。我也会参加拨款撰写课程，以增加更多拨款申请成功的机会。(Iv)发展拨款申请。在奖学金期间，我将申请另外两个资助计划。第一个项目将支持FUNMOVES在全国范围内的推广，以进一步提高“需要额外支持”的分界点的准确性，并使英国各地的儿童能够从快速评估和干预中受益。第二个将与教师、政策制定者和相关利益攸关方共同制定一项针对学校工作人员的培训方案，使他们能够根据FUNMOVES的数据量身定做体育课，以便其学生能够得到有针对性的支持。

The acquisition of motor skills is an essential component of childhood development. A child's ability to use sensory information to guide their movements enables learning about the world around them. One particular group of motor skills which is known to be a risk factor for education and wellbeing outcomes is fundamental movement skills (FMS). Born in Bradford (BiB) have demonstrated that a disproportionately high proportion of children within deprived and ethnic minority communities have FMS problems, yet are less likely to access help for such difficulties. My PhD developed a school-based universal screening tool for FMS (FUNMOVES) to reduce inequalities and expedite the identification of neurodiverse children that would benefit from additional educational support. This fellowship would enable me to extend the reach of FUNMOVES, increasing the likelihood of happy and healthy lifespans for children. This fellowship would also have a significant impact on my professional development. The work within this fellowship will fall within four broad categories:(i) Conducting further research. Data will be collected from children in Years 1-6 across 10 Primary schools in Bradford (n = ~3600). These data will be used to begin understanding what scores on FUNMOVES constitute 'normal development', so that a cut-off value can be applied to identify children with additional needs. During my PhD, children were compared against other children in their year at their school. By gathering data from Primary schools across the district, difficulties identified will be a more accurate representation of the general population. The data collected on these ~3600 children will also be fed into the Connected Bradford dataset, whereby the educational and healthcare records have been linked for all children living in the area. By integrating FUNMOVES information within this dataset, it will enable a large-scale evaluation of the role that childhood motor skills play in inequalities in education and wellbeing.(ii) Sharing my findings. I would host a co-production event to present FUNMOVES to school staff, policy makers (e.g. the Department for Education) and relevant stakeholders. During which I would ask about lived experiences to shape how FUNMOVES can be feasibly scaled up in real-world settings. I would present my findings from the co-production event, and the further research planned at large international conferences which bring together academics from a range of disciplines including Psychology and Education. I would also write these two pieces of work up for (i) publication in leading academic journals and (ii) briefing letters for the Department for Education.(iii) Enhance my research skills and collaborative networks. In order to analyse the data from the additional research outlined above, I would need to gain advanced quantitative analysis skills as a social science researcher. Drawing upon expertise within Computer Sciences at the University of Bradford I would attend courses to improve my data analytic abilities. I would also undertake a study visit at Stanford University within their Population Health Sciences department to improve my ability to work with large-scale datasets and learn about the latest techniques in the field. I would also attend grant writing courses, to increase the likelihood of further funding applications being successful.(iv) Develop funding applications. During the fellowship, I would apply for two further funding schemes. The first will support national roll-out of FUNMOVES, to further increase the accuracy of cut-points for 'needing additional support', and to enable children from across the UK to benefit from expedited assessment and intervention. The second will co-develop with teachers, policy makers and relevant stakeholders, a training programme for school staff which will enable them to tailor Physical Education in response to FUNMOVES data, so that their pupils can receive targeted support.