The project investigates the dynamics of social licence and trust for the operational use of data linkage and predictive analytics to identify families for service intervention. Using algorithms, the characteristics of families who have known poor outcomes for their children are applied to linked up information about all families, to predict which families may have poor outcomes in the future. Individual families can be identified for early intervention by services to prevent the predicted poor outcomes. The research aims to provide an understanding of social licence – legitimacy, acceptance and trust - for these data practices among those implicated: parents of dependent children (<16 years), using a mix of methods to gain a multi-dimensional view. There are three comprising data sets: Survey: online and telephone probability-based panel survey (NatCen) with the focus in principle views on a range of aspects of data linkage and analytics for operational purposes; focus groups: online discussions recorded and transcribed with the focus on articulations and negotiations of perspectives on operational data linkage and predictive analytics for targeted service intervention; and Individual interviews: online discussions recorded and transcribed with a focus on experiences and views of position in relation to data held about their family by services they access, and bases for trust or distrust in operational data linkage and predictive analytics. All datasets are anonymised and consent for use obtained from participants.National and local government departments and services collect and hold information about families, such as taxation, medical records, pupil data, police records. These different data sources can be linked together and used operationally through the application of algorithms to identify individual families for service intervention, with data linkage and analysis carried out in-house or outsourced to private data analytic companies. On the one hand, data linkage and analytics offer more efficient public services based on predictive risk modelling to pre-empt problems, and targeting for enhanced outcomes. One the other hand, issues have been raised about data security, consent, deterring parents from using services, and the extent of public acceptance and trust - known as social licence. This study will fill a vital gap in knowledge about the dynamics of social licence and trust for operational data linkage and analytics among parents of dependent children, in a context where policy developments, and data linkage and analytics practices to inform services interventions may be moving ahead of public knowledge and consent. Specifically it will undertake a series of interlinked systematic and in-depth research activities to provide a multidimensional understanding: 1. Identify the various supportive and critical rationales for data linkage and analytics, predictive risk modelling, and family intervention by conducting an analysis of the content of reports and discussions by national and local government, data analytic companies, charities and advocacy groups, parenting sites, and mainstream media, reports and discussions. 2. Ascertain the consensus among parents about what is acceptable or unacceptable in relation to data linkage and analytics as a basis for risk modelling and intervention in family lives, and any differences between parents from different social groups (e.g. gender, social class, ethnicity) in social licence and trust, through a survey of c. 1000 parents of dependent children. 3. Examine how different social groups of parents articulate and negotiate their perspectives on operational data linkage and analytics, predictive risk modelling, and potential benefits or harms through holding discussions with up to five groups each made up of, for example, mothers or fathers, minority ethnic parents, affluent or disadvantaged parents, urban or rural residents. 4. Explore the specific views and experiences of parents who are engaging with family service interventions on the data held about them, and the parameters of their social licence and bases for trust in operational data linkage and analytics, through individual interviews with up to 20 of them. The research intends to provide a comprehensive, dynamic and multifaceted understanding of parental social licence for and trust in operational data linkage and analytics that can inform public understanding, policy development, and practices in the field of family intervention. It will: - involve a range of experts from academia, statutory and voluntary sectors in an advisory group throughout the study, to help inform the research as it develops; - help to inform public understanding of operational data linkage and analytics, especially parents, including through an informative animated video output; - feed policy maker and advocacy group understandings and considerations of issues in operational data linkage and analytics for family intervention into the development of policy recommendations from the findings through a workshop, and promotion of recommendations including through briefing papers; and - contribute to academic understanding of social licence, trust and operational data linkage and analytics in the field of family services through delivering conference papers and publishing in targeted peer reviewed academic journals.

For the survey: NatCen have their own protocols and remove direct identifiers: https://www.natcen.ac.uk/taking-part/studies-in-field/timeusestudy/privacy-notice/ - For the focus groups and individual interviews: names and places were anonymised, and in some cases where life details may have led to identification these have been deleted/changed. Anonymisation interventions are clear indicated in the transcripts. Participants in individual interviews all had contact with social care and other public service interventions. This means that their interview discussions are of a sensitive nature. Some of the details they disclosed were specific to their individual case and could have led to identification. We have anonymised details to prevent this.