Global mental health and the DIADA project.

The DIADA project, understood as a mental healthcare implementation experience in the context of a middle-income country like Colombia, promotes a necessary discussion about its role in the global mental health framework. The following article outlines the main points by which this relationship occurs, understanding how the project contributes to global mental health and, at the same time, how global mental health nurtures the development of this project. It reflects on aspects like the systematic screening of patients with mental illness, the use of technology in health, the adoption of a collaborative model, the investigation on implementation, a collaborative learning and the Colombian healthcare system. These are all key aspects when interpreting the feedback cycle between the individual and the global. The analysis of these components shows how collaborative learning is a central axis in the growth of global mental health: from the incorporation of methodologies, implementation of models, assessment of outcomes and, finally, the dissemination of results to local, regional and international stakeholders.

Implementing a Redcap-based research data collection system for mental health.

BACKGROUND: The implementation of new technologies in medical research, such as novel big storage systems, has recently gained importance. Electronic data capture is a perfect example as it powerfully facilitates medical research. However, its implementation in resource-limited settings, where basic clinical resources, internet access, and human resources may be reduced might be a problem. METHODS: In this paper we described our approach for building a network architecture for data collection to achieve our objectives using a REDCap® tool in Colombia and provide guidance for data collection in similar settings. CONCLUSIONS: REDCap is a feasible and efficient electronic data capture software to use in similar contexts to Colombia. The software facilitated the whole data management process and is a way to build research capacities in resourced-limited settings.