International Journal of Mental Health Systems (Dec 2010)

Where there is no evidence: use of expert consensus methods to fill the evidence gap in low-income countries and cultural minorities

  • Jorm Anthony F,
  • Minas Harry

DOI
https://doi.org/10.1186/1752-4458-4-33
Journal volume & issue
Vol. 4, no. 1
p. 33

Abstract

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Abstract Background In both developing countries and in relation to cultural minorities there have been calls to scale up mental health services and for evidence-informed policy and practice. Evidence based medicine The evidence based medicine movement has had a major influence in improving practice. However, implementation of this approach has some major difficulties. One that has been neglected is the situation where there is no relevant evidence. This situation is more likely to occur for healthcare decisions in developing countries or for cultural minorities within developed countries, because resources do not exist for expensive research studies. Consensus methods Consensus methods, such as the Delphi process, can be useful in providing an evidence base in situations where there is insufficient evidence. They provide a way of systematically tapping the expertise of people working in the area and give evidence that is readily applicable for a particular country and culture. Although consensus methods are often thought of as low in the hierarchy of evidence, consensus is central to the scientific process. We present four examples where the Delphi method was used to assess expert consensus in situations where no other evidence existed: estimating the prevalence of dementia in developing countries, developing mental health first aid guidelines in Asian countries, mental health first aid guidelines for Australian Aboriginal people, and modification of the concept of 'recovery' for Australian immigrant communities. Conclusion Consensus methods can provide a basis for decision-making and considered action when there is no evidence or when there are doubts about the applicability of evidence that has been generated from other populations or health system settings.