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

犯罪轨迹的教育和社会关怀预测因素：行政数据关联研究

• 批准号: ES/W002647/1
• 负责人: Hannah Dickson
• 金额: $ 20.55万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FW002647%2F1
• 关键词: Education; social; care; predictors; offending

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.

犯罪行为是一个全球性的公共卫生问题，与受害者和犯罪者的广泛的健康和社会后果有关。这种行为通常遵循不同的途径或轨迹，有些人一生都有反社会行为，而另一些人则只有很短的时间，如在青春期。然而，准确预测儿童或青少年个体可能遵循的路径仍然很困难。准确预测那些一生中最有可能采取反社会行为的人，将有助于为教育、社会护理或刑事司法环境中的有针对性的干预措施提供信息。收集到的教育和社会保健数据可能对这些预测非常重要。在联合王国，接受公立教育的每个儿童都可获得关于学习发展、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。这些信息现在可以与儿童后来的犯罪记录联系起来。这种类型的数据具有巨大的潜在公共利益，因为它真正代表了整个人口，而且由于数据已经收集，因此具有很高的成本效益。这项研究将确定是否有可能使用例行收集的教育和社会护理数据，以确定那些儿童和青少年谁更有可能成为惯犯之前，参与刑事司法系统开始。这将有助于影响关于如何最好地支持他们的决定，有可能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，我将使用定期收集的10-32岁的个人犯罪记录信息，这些个人出生于1985年8月31日或之后，并包括2007年8月31日，这些信息与4至18岁的同一个人以前的教育和社会护理记录有关。首先，我将使用一种被称为潜在类别分析的统计分析方法来识别第一次定罪或警告后的犯罪行为的不同轨迹。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，看看是否有可能利用先前的教育和社会关怀信息来预测第一步中确定的犯罪轨迹。通过机器学习，计算机可以学习做出决策和预测，而无需直接编程，并且可以潜在地识别我们作为人类可能错过的重要因素。查明持续犯罪风险较高的儿童和青少年将被用于为早期干预方法和刑事司法对策提供信息，以减少犯罪，并进而促进循证决策。该项目的研究结果还将强调如何利用定期收集的数据来改善对儿童和青少年的公共服务。为了充分实现这些好处，我将在ADR UK，司法部和教育部网站上发表我的项目工作。我将制作两份学术出版物，并在项目过程中向其他学者和项目受益人进行一系列演讲。我还计划与青年慈善组织和更广泛的项目利益相关者进行接触，讨论项目成果，并讨论这项工作的未来方向。例如，与利益攸关方协商后，该项目的调查结果可用于为开发一种工具提供信息，该工具可以区分教育环境中未来犯罪轨迹的可能性，这将有助于儿童尽早获得所需的帮助。

项目成果

Education and social care predictors of offending trajectories: A UK administrative data linkage study.

• DOI: 10.23889/ijpds.v7i3.1928
• 发表时间: 2022-08-25
• 期刊: International Journal of Population Data Science

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

犯罪轨迹的教育和社会关怀预测因素：英国行政数据关联研究

• DOI: 10.23889/ijpds.v8i2.2206
• 发表时间: 2023
• 期刊: International Journal of Population Data Science
• 作者: Dickson H