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Using the Nominal Group Technique to determine a nursing framework for a forensic mental health service: A discussion paper.

Tessa MaguireLoretta GarveyJo RyanMichael OlasojiGeorgina Willets
Published in: International journal of mental health nursing (2022)
The Nominal Group Technique is a method used to explore issues, generate ideas, and reach consensus on a topic. The Nominal Group Technique includes individual and group work and is designed to ensure participants have the same opportunity to engage and provide their opinions. While the technique has been used for around six decades to assist groups, in industry, and government organizations to examine issues and make decisions, this technique has received limited attention in nursing research, particularly in mental health. This discussion paper describes the use of a modified Nominal Group Technique for a study designed to determine a nursing decision-making framework for a state-wide forensic mental health service. Modifications were made to the traditional technique, to enable participants to make an informed and collective decision about a suitable framework for the novice to expert nurses, across secure inpatient, prison, and community forensic mental health settings. The Nominal Group Technique generated rich data and offered a structured approach to the process. We argue that the Nominal Group Technique offers an exciting and interactive method for nursing research and can increase opportunity for minority group members to participate. This technique also offers a time efficient way to engage busy clinical nurses to participate in research, with the advantage of members knowing the decision on the day of the group. Consideration, however, needs to be given to the duration and effect on participant concentration, and if not actively managed by facilitators, the possible emergence of group dynamics affecting individuals' decisions.
Keyphrases
  • mental health
  • healthcare
  • decision making
  • mental illness
  • palliative care
  • quality improvement
  • data analysis