Acceptability and feasibility of digital technology for training community health workers to deliver brief psychological treatment for depression in rural India

2019 
Abstract INTRODUCTION Digital technology offers opportunities to train community health workers to deliver psychological treatments towards closing the gap in existing mental health services in low-resource settings. This study explored the acceptability and feasibility of using digital technology for training community health workers to deliver evidence-based brief psychological treatment for depression in rural India. METHODS This study consisted of two sequential evaluations of digital training prototypes using focus group discussions to explore community health worker perspectives about the digital training platform and the program content. Through an iterative design process, feedback was collected about the first prototype to inform modifications to the second prototype. Qualitative data was analyzed using a framework analysis approach. RESULTS Thirty-two community health workers participated in three separate focus group discussions. Five overarching themes related to acceptability and feasibility of digital training revealed that training on detection and treatment of depression was considered important by study participants for addressing ‘stress’ and ‘tension’ within their communities, while the digital platform was viewed as useful and convenient despite limited familiarity with using digital technology. Moreover, participants suggested simple language for the program and use of interactive content and images to increase interest and improve engagement. DISCUSSION Digital technology appears acceptable and feasible for supporting training of community health workers to deliver evidence-based depression care in rural India. These findings can inform use of technology as a tool for developing the clinical skills of community health workers for treating depression in low-resource settings.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    36
    References
    18
    Citations
    NaN
    KQI
    []