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Home arrow Language & Literature arrow The Palgrave Handbook of Sociocultural Perspectives on Global Mental Health


Global research on mental health is growing. However, local relevance of international epidemiological studies is limited. Furthermore, individuals with mental disorders, especially those who are poor, are marginalised due to stigma, their needs and valuable insights often overlooked by researchers and policy makers (Patel and Kleinman 2003). Of the small amount of internationally accessible mental health research conducted in LMIC, very little is practice based and people living with mental disorders remain largely unheard (Sharan et al. 2006).

BasicNeeds regards an evidence-based approach to improving practice and policy as highly important. The research module helps to embed a research ‘culture’ into the operations of the MHD model. The module facilitates evidence generation from field operations and also brings forth views of those affected by mental illness. The research module has two components.

Quality Assurance and Impact Assessment System

A standardised semi-automated quality assurance-impact assessment (QA-IA) system operates across BasicNeeds’ programmes to monitor progress, reach and quality of implementation and to assess impact. Detailed protocols and standardised tools guide data collection. Both quantitative and qualitative data are collected.

Quantitative data comprises data of individual beneficiaries collected at baseline (i.e., at the time of their joining the BasicNeeds programme) and follow-up (collected annually from a sample of the total beneficiaries), and data of field implementation, that is, data on every activity carried out recorded regularly and systematically together with costs. Qualitative data comprises Life Stories and Participatory Data Analysis reports. The Life Stories are akin to semi-structured in-depth narrative interviews. Life stories of purposively selected beneficiaries are collected at baseline and updated annually to record narrations of their experience and perceptions. Participatory data analysis is an inclusive research process where participants evaluate the services they receive and also the change they experience. In a typical session, affected persons and their carers analyse their own data and discuss and summarise them under predetermined themes, often based on the MHD model, and suggest very practical recommendations. Through this method, they evaluate the intervention activities of the MHD model (Raja et al. 2012).

The quantitative analysis includes quarterly statistics assessing the progress of individuals: an analysis of implementation costs and results and an assessment of the scale of the programme. These are collected at a country level and are drawn annually into the organisation-wide impact report alongside qualitative analysis.

Providing econometric analysis to establish the impact of the MHD programmes is challenging given the scale and complexity of the operational context. Collecting and collating routine good quality data from every user from programmes across several countries is a huge task. Standardised high-quality training to field personnel and systematic quality checks at various stages minimises errors. Furthermore measuring cost per beneficiary is complicated by beneficiaries often accessing different interventions multiple times with varied frequencies. Detailed cost analysis is therefore done by activity factoring in also personnel time. This provides the basis for total cost per beneficiary calculations during a given period in a particular programme site.

A mix of qualitative and quantitative evidence combined with anecdotal narrations from different stakeholders provides a comprehensive picture of the actions and impact of the MHD model. Additionally, to mitigate the limitations of methodological rigour posed by the QA-IA system, an in-depth evaluation (mostly external) of the model is undertaken from time to time in different programme sites.

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