Home Office / ADR UK Feasibility Study Lead Academic
内政部 / ADR UK 可行性研究主管学术
基本信息
- 批准号:ES/V002929/1
- 负责人:
- 金额:$ 10.14万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2020
- 资助国家:英国
- 起止时间:2020 至 无数据
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Rates of serious violent crime in England and Wales have been increasing since 2014. Although these offences account for only around 1% of total crime, they cause disproportionate harm to individuals and society as a whole. Because of this, tackling serious violence is a UK Government and police priority. It is increasingly recognised that violence is preventable and that the most effective ways to prevent violent crime are not directly related to the policing or criminal justice systems. New strategies aimed at reducing violence seek to tackle upstream risk factors, thus preventing the development of offending behaviour among young people. However, there is currently little evidence regarding what types of intervention are effective. A total of £200 million has been granted to the Youth Endowment Fund (YEF) over the next 10 years to support early interventions; evaluation of these interventions is an integral part of the programme.In order to generate high quality evidence, rigorous evaluation of interventions is crucial. There are two key issues that will impact on the quality of such evaluations. Firstly, there is a need for valid, reliable data sources - that measure outcomes prior to and after the intervention has been implemented. Ideally, the data would include both short-term and long-term outcomes. Secondly, it is important to have a well-matched comparison group. Without this, it is difficult to draw any clear conclusions about the effect of the intervention because any changes in rates of offending could arise as a result of other factors (i.e. may not be due to the intervention itself). With this in mind, the Ministry of Justice (MoJ) and Department for Education (DfE) are linking key national datasets, bringing together data from the criminal justice system, including police, prison, and court records, with data from the education system, such as school attainment, absence and exclusions. The linked dataset will contain around 15 years' of data on around 20 million individuals and will have the potential to form a resource to allow robust evaluation of YEF and other interventions.This study has two main elements. In the first stage we will evaluate and document the quality and scope of the MoJ-DfE linked dataset. In the second stage we will investigate the feasibility of using the linked dataset to generate matched control groups for the purpose of evaluating interventions aimed at reducing offending rates in young people; we will compare two different statistical approaches to doing this.Our findings will inform the future development and use of the dataset.
自2014年以来,英格兰和威尔士的严重暴力犯罪率一直在上升。虽然这些犯罪仅占犯罪总数的1%左右,但它们对个人和整个社会造成了不成比例的伤害。因此,解决严重暴力问题是联合王国政府和警察的优先事项。人们日益认识到,暴力是可以预防的,预防暴力犯罪的最有效方法与警务或刑事司法系统没有直接关系。旨在减少暴力行为的新战略力求解决上游风险因素,从而防止青年人形成犯罪行为。然而,目前几乎没有证据表明什么样的干预措施是有效的。未来10年,青年捐赠基金(YEF)将获得总计2亿英镑的资金,以支持早期干预措施;对这些干预措施的评估是该计划不可或缺的一部分。为了产生高质量的证据,对干预措施进行严格的评估至关重要。有两个关键问题将影响到这种评价的质量。首先,需要有效、可靠的数据来源-衡量干预措施实施前后的成果。理想情况下,数据将包括短期和长期结果。其次,重要的是要有一个匹配良好的对照组。没有这一点,就很难就干预措施的效果得出任何明确的结论,因为犯罪率的任何变化都可能是其他因素造成的(即可能不是干预措施本身造成的)。考虑到这一点,司法部和教育部正在将关键的国家数据集联系起来,将刑事司法系统的数据(包括警察、监狱和法院记录)与教育系统的数据(如学业成绩、缺勤和被开除情况)汇集在一起。该关联数据集将包含约2,000万人的约15年数据,并将有可能形成一个资源,以便对YEF和其他干预措施进行可靠的评估。在第一阶段,我们将评估和记录MoJ-DfE链接数据集的质量和范围。在第二阶段,我们将研究使用关联数据集生成匹配对照组的可行性,以评估旨在降低青少年犯罪率的干预措施;我们将比较两种不同的统计方法,我们的研究结果将为数据集的未来开发和使用提供信息。
项目成果
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
School to prison pipelines: Associations between school exclusion, neurodisability and age of first conviction in male prisoners
学校到监狱的管道:学校排斥、神经障碍和男性囚犯首次定罪年龄之间的关联
- DOI:10.1016/j.fsiml.2023.100123
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Kent H
- 通讯作者:Kent H
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Rosaleen Peggy Cornish其他文献
Rosaleen Peggy Cornish的其他文献
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{{ truncateString('Rosaleen Peggy Cornish', 18)}}的其他基金
The impact of childhood adversity on violent crime in adolescence and early adulthood
童年逆境对青春期和成年早期暴力犯罪的影响
- 批准号:
ES/T014393/1 - 财政年份:2021
- 资助金额:
$ 10.14万 - 项目类别:
Research Grant
Using linked health and administrative data to reduce bias due to missing data and measurement error in observational research
使用关联的健康和管理数据来减少观察研究中由于缺失数据和测量误差而导致的偏差
- 批准号:
MR/L012081/1 - 财政年份:2014
- 资助金额:
$ 10.14万 - 项目类别:
Fellowship
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