Abstract
Social work case files hold rich detail about the lives and needs of vulnerable groups. Traditional case-reading studies to gain generalisable knowledge are resource-intensive, however, and sample sizes thereby limited. The advent of ‘big data’ technology, and vast repositories of centrally stored electronic records offer social work researchers novel alternatives, including data linkage and predictive risk modelling using administrative data. Free-text documents, however – including assessments, reports, and case chronologies – remain a largely untapped resource. This paper describes how 5000 social work court statements held by the Child and Family Court Advisory Support Service in England (Cafcass) were analysed using natural language processing (NLP) based on simple rules and mathematical principles. Thirteen factors relating to harm and risk to children involved in care proceedings in England were identified by automated computer techniques, and almost 90% agreement with professional readers achieved when the factors were clear-cut. The study represents an innovative approach for social work research on complex social problems. In conclusion, the paper discusses learning points; practical implications; future research avenues; and the technical and ethical challenges of NLP.
| Original language | English |
|---|---|
| Pages (from-to) | 222-235 |
| Number of pages | 14 |
| Journal | Methodological Innovations |
| Volume | 15 |
| Issue number | 3 |
| Early online date | 12 Aug 2022 |
| DOIs | |
| Publication status | Published (in print/issue) - 30 Nov 2022 |
Bibliographical note
Funding Information:We are grateful to Cafcass for supporting this study and making their data available and to Dr John Mallett, Ulster University, for support and advice during this project. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by a PhD Scholarship from the Department of Education and Learning for Northern Ireland.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by a PhD Scholarship from the Department of Education and Learning for Northern Ireland.
Publisher Copyright:
© The Author(s) 2022.
Funding
Funding Information: We are grateful to Cafcass for supporting this study and making their data available and to Dr John Mallett, Ulster University, for support and advice during this project. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by a PhD Scholarship from the Department of Education and Learning for Northern Ireland. Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was funded by a PhD Scholarship from the Department of Education and Learning for Northern Ireland. Publisher Copyright: © The Author(s) 2022.
Keywords
- Algorithm
- automation
- big data
- child protection
- decision-making
- ural language processing
- NLP
- predictive analytics
- predictive risk modelling
- PRM
- rules-based
- social work
