Experiences of staff managing self-harm algorithmically
Abstract
Purpose
An algorithmic approach to managing self-harm has been introduced within a women's enhanced medium secure. This paper explores the experiences and perspectives of nursing staff using the model.
Design/methodology/approach
Purposive sampling was used to gather the experiences of a cross-section of nursing staff of different grades. Semi-structured interviews collected data relating to their experiences using the model as well as their satisfaction with the model in terms of its effectiveness and safety for staff and patients.
Findings
Nursing staff described themselves as being confident with the model and were clearly implementing it safely and effectively. They described the model as addressing the challenge of managing self-harm alongside the risk of violence, and also described the importance of effectively marrying individualised assessment and planning with the algorithmic approach. The difficulty for staff new to the ward was also described and this is a useful focus for further development and evaluation.
Practical implications
Nursing staff describe the algorithmic approach to managing self-harm in use on this ward as safe and effective and it could usefully be trialled in other areas which manage difficult and potentially high-lethality self-harm.
Originality/value
The algorithmic model is a new approach to dealing with the challenging levels of self-harm within the service, and there is a clear need to ensure that the end-users of model are confident that they are using it safely and effectively. This paper describes this work as well as expanding on some of the complexities of managing self-harm day-to-day in this challenging environment.
Keywords
Citation
Beeley, C. and Sarkar, J. (2013), "Experiences of staff managing self-harm algorithmically", The Journal of Forensic Practice, Vol. 15 No. 4, pp. 249-258. https://doi.org/10.1108/JFP-08-2012-0008
Publisher
:Emerald Group Publishing Limited
Copyright © 2013, Emerald Group Publishing Limited