Abstract
Background: There is growing global need for scalable approaches to training and supervising primary care workers to deliver mental health services. Over the past decade, the World Health Organization mental health Gap Action Programme Implementation Guide (mhGAP-IG) and associated training and implementation guidance have been disseminated to more than 100 countries. Drawing upon the opportunities provided by mobile technology, an updated electronic version of the mhGAP-IG (e-mhGAP-IG) is now being developed along with a clinical dashboard and guidance for use of mobile technology in supervision.
Objectives: This study will assess the feasibility, acceptability, adoption, and other implementation parameters of the e-mhGAP-IG for diagnosis and management of depression in two low- and middle-income countries (Nepal and Nigeria), as well as conduct a feasibility cluster randomised control trial (cRCT) to evaluate trial procedures for a subsequent fully-powered trial comparing the clinical and cost-effectiveness of e-mhGAP-IG and remote supervision with standard mhGAP implementation.
Methods: A feasibility cRCT will be conducted in Nepal and Nigeria to evaluate the feasibility of the e-mhGAP-IG for use in depression diagnosis and treatment. In each country, an estimated 20 primary health clinics (PHCs) in Nepal and 6 PHCs in Nigeria will be randomized to have their staff trained in e-mhGAP-IG or the paper version of mhGAP-IG v2.0. The PHC will be the unit of clustering. All primary care workers (PCWs) within a facility will receive the same training (e-mhGAP-IG vs. paper mhGAP-IG). Approximately 2-5 PCWs, depending on staffing, will be recruited per clinic (estimated n=20 health workers per arm in Nepal and 15 per arm in Nigeria). The primary outcomes of interest will be the feasibility and acceptability of training, supervision, and care delivery using e-mhGAP-IG. Secondary implementation outcomes include adoption of the e-mhGAP-IG and feasibility of trial procedures. The secondary intervention outcome—and primary outcome for a subsequent fully-powered trial—will be the accurate identification of depression by PCWs. Detection rates before and after training will be compared in each arm.
Results: To date, qualitative formative work has been conducted in both sites to prepare for the pilot cRCT, and the e-mhGAP-IG and remote supervision guidance have been developed.
Conclusions: Incorporation of mobile digital technology has the potential to improve the scalability of mental health services in primary care and enhance the quality and accuracy of care.
Objectives: This study will assess the feasibility, acceptability, adoption, and other implementation parameters of the e-mhGAP-IG for diagnosis and management of depression in two low- and middle-income countries (Nepal and Nigeria), as well as conduct a feasibility cluster randomised control trial (cRCT) to evaluate trial procedures for a subsequent fully-powered trial comparing the clinical and cost-effectiveness of e-mhGAP-IG and remote supervision with standard mhGAP implementation.
Methods: A feasibility cRCT will be conducted in Nepal and Nigeria to evaluate the feasibility of the e-mhGAP-IG for use in depression diagnosis and treatment. In each country, an estimated 20 primary health clinics (PHCs) in Nepal and 6 PHCs in Nigeria will be randomized to have their staff trained in e-mhGAP-IG or the paper version of mhGAP-IG v2.0. The PHC will be the unit of clustering. All primary care workers (PCWs) within a facility will receive the same training (e-mhGAP-IG vs. paper mhGAP-IG). Approximately 2-5 PCWs, depending on staffing, will be recruited per clinic (estimated n=20 health workers per arm in Nepal and 15 per arm in Nigeria). The primary outcomes of interest will be the feasibility and acceptability of training, supervision, and care delivery using e-mhGAP-IG. Secondary implementation outcomes include adoption of the e-mhGAP-IG and feasibility of trial procedures. The secondary intervention outcome—and primary outcome for a subsequent fully-powered trial—will be the accurate identification of depression by PCWs. Detection rates before and after training will be compared in each arm.
Results: To date, qualitative formative work has been conducted in both sites to prepare for the pilot cRCT, and the e-mhGAP-IG and remote supervision guidance have been developed.
Conclusions: Incorporation of mobile digital technology has the potential to improve the scalability of mental health services in primary care and enhance the quality and accuracy of care.
| Original language | English |
|---|---|
| Journal | JMIR research protocols |
| Publication status | Published - 1 Jun 2021 |
Keywords
- Mental health
- community mental health
- Digital technology
- Primary health care
- intervention
- ehealth
- mhealth
- LMIC
- Remote supervision
- Training
