Education and social care predictors of offending trajectories: An administrative data linkage study

犯罪行为是一个全球公共卫生问题，与受害者和肇事者的各种不良健康和社会后果有关。这种行为通常遵循不同的路径或轨迹，一些人在一生中表现出反社会行为，而另一些人只在很短的一段时间内表现出来，比如在他们的青春期。然而，准确预测单个儿童或青少年可能走哪条路仍然很困难。对那些一生中最有可能做出反社会行为的人进行准确预测，将有助于在教育、社会护理或刑事司法环境中进行有针对性的干预。常规收集的教育和社会护理数据可能对这种预测非常重要。有关学习发展、学业成绩、学校排斥、儿童保护参与和特殊教育需求的重要信息可供英国国立教育的每一名儿童使用。这样的信息现在可以与孩子后来的违法记录联系起来。这种类型的数据具有巨大的潜在公共利益，因为它真正代表了全体人口，而且由于已经收集了数据，因此具有极高的成本效益。这项研究将确定是否有可能利用常规收集的教育和社会照料数据，在开始参与刑事司法系统之前确定哪些儿童和青少年更有可能成为惯犯。这将有助于影响如何最好地支持他们的决定，潜在地减少刑事犯罪及其相关的社会和经济成本。在这个为期12个月的项目中，我将使用从1985年8月31日至2007年8月31日(包括2007年8月31日)10-32岁出生的个人的犯罪记录的常规收集的信息，这些信息与这些人在4至18岁之间以前的教育和社会照料记录相关联。首先，我将使用一种名为潜在类别分析的统计分析方法，来识别在第一次记录的定罪或警示之后的不同违规行为轨迹。其次，我将采用一种统计学习方法，这是机器学习的一种形式，看看是否有可能使用先前的教育和社会关怀信息来预测第一步中确定的违规轨迹。有了机器学习，计算机可以学习做出决定和预测，而不需要直接编程这样做，并且可以潜在地识别我们作为人类可能遗漏的重要因素。确定持续犯罪风险较高的儿童和青少年将被用来为早期干预办法和刑事司法对策提供信息，以减少犯罪，进而有助于基于证据的政策制定。该项目的调查结果还将强调如何使用常规收集的数据来改善为儿童和青少年提供的公共服务。为了充分实现这些好处，我将记录我在该项目上的工作，并在ADR UK、司法部和教育部的网站上发表。我将出版两份学术出版物，并在项目过程中向其他学者和项目受益者发表一系列演讲。我还计划与青年慈善组织和更广泛的项目利益攸关方接触，讨论项目结果和这项工作的未来方向。例如，在与利益攸关方协商后，这个项目的结果可以被用来为开发一种工具提供信息，该工具可以区分未来在教育环境中出现违规轨迹的可能性，这将帮助儿童在早期获得他们需要的帮助。

Criminal behaviour is a global public health problem associated with a wide range of poor health and social outcomes for victims and perpetrators. Such behaviour typically follows distinct pathways or trajectories, with some individuals behaving antisocially throughout their life, and others for only short periods of time such as during their adolescence. However, accurately predicting which pathway an individual child or adolescent may follow remains difficult. Accurate prediction of those who are most at risk of behaving antisocially throughout their life would help to inform targeted interventions in educational, social care or criminal justice settings. Routinely collected educational and social care data may be very important in informing such predictions. Important information on learning development, school attainment, school exclusion, child protection involvement and special educational need is available for every child in state education in the UK. Such information can now be linked with the child's later offending records. This type of data has enormous potential public benefits by being truly representative of the whole population and highly cost effective because the data has already been collected. This study will establish whether it is possible to use routinely collected education and social care data to identify those children and adolescents who are more likely to become persistent offenders before involvement with the criminal justice system begins. This will help influence decisions on how best to support them, potentially reducing criminal offending and its associated social and economic costs. In this 12-month project I will use routinely collected information on crime records for individuals aged 10-32 years born on, or after, 31st August 1985 p to and including 31st August 2007 which are linked to the same individuals' prior educational and social care records when aged between 4 and 18 years. First, I will use a statistical analysis approach called latent class analysis to identify different trajectories of offending behaviours following a first recorded conviction or caution. Second, I will adopt a statistical learning approach, which is a form of machine learning, to see if it is possible to predict the offending trajectories identified in step 1, using prior education and social care information. With machine learning, computers can learn to make decisions and predictions without being directly programmed to do so and can potentially identify important factors that we as humans may miss. The identification of children and adolescents at higher risk for persistent offending will be used to inform early intervention approaches and criminal justice responses to reduce offending and by extension contribute to evidence-based policy making. The project findings will also highlight how routinely collected data can be used to improve public services for children and adolescents. In order for these benefits to fully realised, I will document my work on the project for publication on ADR UK, Ministry of Justice and Department of Education websites. I will produce two academic publications and give a series of presentations over the course of the project to other academics and project beneficiaries. I also plan to engage with youth charity organisations and wider project stakeholders to discuss project results and discuss future directions of this work. For example, findings from this project, in consultation with stakeholders, could be used to inform the development of a tool that could discriminate between the likelihood of future offending trajectories in an educational setting, that will help children get the help they need early on.