Development and Evaluation of a Web Application to Support Remote Clinical Supervision of Lay Counselors in Kenya
Psychological treatments delivered by lay counselors, individuals with little or no previous mental health training, have been shown to be effective in treating a range of mental health problems. However, in low resource settings, the dearth of available experts to train and supervise lay counselors is a key bottleneck in scaling up lay counselor delivered psychological treatments. Locally sustainable solutions that allow experts to train and supervise large volume of lay providers are needed. Two proposed solutions include the use of digital health strategies and peer supervision. In study one, we used a human-centered design approach to develop a web application to support asynchronous clinical supervision of lay counselors providing a family therapy program in Kenya. The development process engaged seven previously trained lay counselors and three prior supervisors in a phased design process which resulted in a final application prototype, “REACH”, that supported audio and text communication via a chat and a structured session report form. In study two, we conducted a feasibility study with 30 counselors with the aim of describing the capacity of REACH support supervision practices, exploring the feasibility and acceptability of REACH compared to peer group supervision, and describing the treatment fidelity and clinical competency of a small group of counselors using REACH. REACH was perceived as highly acceptable from both peers and supervisors and demonstrated promising impacts on counselor fidelity and clinical competency. Content analysis of correspondence between the counselors and the superior via the REACH indicated the supervisor was able to implement a wide range of evidence-based supervision strategies. Limitations of bi-directional communication on potential supervision effectiveness are discussed. Overall, this project suggests digital asynchronous clinical supervision holds promise as a scalable method of clinical supervision in low-resource contexts.
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Duke Dissertations
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info