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Coercive containment measures for the management of self-cutting versus general disturbed behaviour: Differences in use and attitudes among mental health nursing staff.

Geoffrey L DickensLeah Hosie
Published in: International journal of mental health nursing (2022)
Self-harm is common in mental health facilities, and coercive containment measures are sometimes used to manage it. Nurses' attitudes towards these measures have been investigated in relation to disturbed behaviour in general, but rarely to self-harm specifically. We therefore investigated mental health nurses' use of and attitudes towards coercive measures (seclusion, restraint, intermittent and constant observations, forced intramuscular medication, and PRN medication) for self-cutting management compared with for disturbed behaviours in general using a cross-sectional, repeated measures survey design. Participants were N = 164 mental health nursing staff. Data collection was via a questionnaire comprising validated attitudinal measures. The study is reported in line with STROBE guidelines. Physical restraint (36.6%), forced intramuscular medication (32.3%) and seclusion (48.2%) had reportedly been used by individuals for self-cutting management. Respondents disapproved of using each coercive measure for self-cutting more than they did for disturbed behaviour in general with the exception of PRN medication. Attitudes to coercive measures differed across target behaviours. Hence, nurses who had used each measure for managing self-cutting disapproved of it less for that purpose than those who had not. Nurses who had used coercive techniques for self-cutting management had less desirable attitudes to their use. We cannot say whether prior use of these techniques led to increased approval or whether greater approval led to an increased willingness to use them. Reducing the use of coercive techniques for self-harm will require attitudes that support its use to be challenged. Less coercive techniques should be encouraged. Harm reduction techniques offer one such alternative.
Keyphrases
  • mental health
  • mental illness
  • healthcare
  • emergency department
  • high resolution
  • machine learning
  • stress induced
  • artificial intelligence
  • electronic health record