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Depression and distress is not detected in the majority of patients seen by nurses, study suggests

Date:
October 4, 2010
Source:
University of Leicester
Summary:
New research reveals that nursing staff have ‘considerable difficulty’ detecting depression and distress in patients.

New research from the University of Leicester reveals that nursing staff have 'considerable difficulty' detecting depression and distress in patients.

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Two new research studies led by Dr Alex Mitchell, consultant in psycho-oncology at Leicestershire Partnership Trust and honorary senior lecturer at the University of Leicester, highlight the fact that while nurses are at the front line of caring for people, they receive little training in mental health.

The researchers call for the development of short, simple methods to identify mood problems as a way of providing more targeted and appropriate treatment for patients.

Dr Mitchell, of the Department of Cancer Studies and Molecular Medicine at the University of Leicester, said: "In terms of dealing with distress and depression, nursing staff are probably the most important group of health professionals.

"In the NHS 400,000 nurses provide valuable support to those suffering a range a physical and mental illnesses but struggle to detect depression in the early stages. Nurses are often very capable of forming good therapeutic relationships and provide a great deal of psychological support which is highly valued. However their ability to do this is increasingly under-pressure from high workloads and little funding for professional development.

"Our first analysis found that 7000 nurses and nursing assistants often overlooked depression in clinical settings. Nurses working in hospital settings and nursing homes correctly identified about 4 out of 10 people with depression and practice nurses working in primary care correctly identified only one in four people with depression."

A second study examined the ability of nurses to detect distressed patients and found half were missed until distress became severe.

Dr Mitchell said the research discovered a number of reasons that accounted for this situation: "Factors that appear to be influential include greater empathy, more confidence with mental health and more time spent with patients. However most nursing staff receive little training in mental health and report low experience in this area. It may be unrealistic to expect nurses to remember complex criteria for detection of depression or to apply lengthy screening tools. In the future we may focus more on who has impaired function and who needs help rather than depression alone."

Dr Mitchell's team is working on short, simple methods to identify mood problems and these can be freely accessed at www.psycho-oncology.info/


Story Source:

The above story is based on materials provided by University of Leicester. Note: Materials may be edited for content and length.


Journal References:

  1. Alex J. Mitchell, Venkatraghavan Kakkadasam. Ability of nurses to identify depression in primary care, secondary care and nursing homes—A meta-analysis of routine clinical accuracy. International Journal of Nursing Studies, 2010; DOI: 10.1016/j.ijnurstu.2010.05.012
  2. Alex J. Mitchell, Nadia Hussain, Lorraine Grainger, Paul Symonds. Identification of patient-reported distress by clinical nurse specialists in routine oncology practice: a multicentre UK study. Psycho-Oncology, 2010; DOI: 10.1002/pon.1815

Cite This Page:

University of Leicester. "Depression and distress is not detected in the majority of patients seen by nurses, study suggests." ScienceDaily. ScienceDaily, 4 October 2010. <www.sciencedaily.com/releases/2010/10/101004101443.htm>.
University of Leicester. (2010, October 4). Depression and distress is not detected in the majority of patients seen by nurses, study suggests. ScienceDaily. Retrieved January 31, 2015 from www.sciencedaily.com/releases/2010/10/101004101443.htm
University of Leicester. "Depression and distress is not detected in the majority of patients seen by nurses, study suggests." ScienceDaily. www.sciencedaily.com/releases/2010/10/101004101443.htm (accessed January 31, 2015).

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