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Forensic risk assessment in people with learning disabilities: principles and process.

  • Published In: Learning Disability Practice, 2024, v. 27, n. 6. P. 26 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: McAleer, Paul 3 of 3

Abstract

Why you should read this article: • To refresh your knowledge of the principles of forensic risk assessment • To recognise the benefits of, and the different evidence-based approaches to, forensic risk assessment • To increase your understanding of risk formulation and the development of a tailored risk management plan. While forensic healthcare is a highly specialised area of clinical practice, many learning disability nurses will, over the course of their careers, be required to provide care to people who have had contact with the criminal justice system. Robust risk assessment and risk management systems are central to ensuring the well-being and safety of people with learning disabilities who have forensic needs. Although the core assessment skills required to undertake forensic risk assessments are embedded in nurses’ skill set, learning disability nurses need to develop their knowledge and understanding of how to apply these skills in forensic practice. This article explores the principles of forensic risk assessments and the elements of the forensic risk assessment process as a foundation to risk formulation. The author uses a fictitious case study to illustrate forensic risk assessment, risk formulation and the development of a tailored risk management plan. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Learning Disability Practice. 2024/12, Vol. 27, Issue 6, p26
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2024
  • ISSN:1465-8712
  • DOI:10.7748/ldp.2024.e2233
  • Accession Number:181524554
  • Copyright Statement:Copyright of Learning Disability Practice is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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