Suchergebnisse

India was one of the major World Health Organization (WHO) member countries to launch its National Mental Health Programme (NMHP) in 1982 in accordance with WHO's recommendations to deliver mental health services to the people under the framework of general health care system in the community. NMHP underwent major strategic revisions over its course, starting from setting a district as the unit for program planning and implementation under the District Mental Health Program (DMHP) to incorporating it with the National Rural Health Mission (NRHM) for effectively scaling up the program. The program also underwent evaluations by government bodies and independent agencies and was reviewed by many researchers. The program has been partly successful in terms of enhancing its reach to community, improving service delivery, and getting increased budgetary allocation, but at the same time, its impact was limited by financial and human resource constraints, lack of community participation, ineffective training, poor NGO/private partnership, and lack of a robust monitoring and evaluation (M and E) system. The latest National Mental Health Policy and the incorporation of its objectives have given a new impetus to the ongoing NMHP, however, its implementation needs to be monitored and the impact is yet to be evaluated. We attempted to review the available literature pertaining to NMHP and DMHP to highlight the determinants of its outcome, with special emphasis on on-going programs and to provide some important future directions.

Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This article explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level ("none," "mild," "moderate," and "severe"). Between-group analysis shows that discriminating ECG features (i.e., heart rate and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques. The findings need to be further scrutinized in real-world settings for the generalizability of the results.