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. 2020 Jun;55(3):425-434.
doi: 10.1002/ijop.12604. Epub 2019 Jun 17.

Validating mental health assessment in Kenya using an innovative gold standard

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Validating mental health assessment in Kenya using an innovative gold standard

Leah K Watson et al. Int J Psychol. 2020 Jun.

Abstract

With the growing burden of mental health disorders worldwide, alongside efforts to expand availability of evidence-based interventions, strategies are needed to ensure accurate identification of individuals suffering from mental disorders. Efforts to locally validate mental health assessments are of particular value, yet gold-standard clinical validation is costly, time-intensive, and reliant on available professionals. This study aimed to validate assessment items for mental distress in Kenya, using an innovative gold standard and a combination of culturally adapted and locally developed items. The mixed-method study drew on surveys and semi-structured interviews, conducted by lay interviewers, with 48 caregivers. Interviews were used to designate mental health "cases" or "non-cases" based on emotional health problems, identified through a collaborative clinical rating process with local input. Individual mental health survey items were evaluated for their ability to discriminate between cases and non-cases. Discriminant survey items included 23 items adapted from existing mental health assessment tools, as well as 6 new items developed for the specific cultural context. When items were combined into a scale, results showed good psychometric properties. The use of clinically rated semi-structured interviews provides a promising alternative gold standard that can help address the challenges of conducting diagnostic clinical validation in low-resource settings.

Keywords: Family functioning; Kenya; Measure development; Mental health; Validation.

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Conflict of interest statement

Conflicts of Interest: The authors declare that they have no conflict of interest.

Figures

Figure 1:
Figure 1:
Mental health survey items by gradient score based on ability to discriminate mental health cases from non-cases (n = 33).

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