Supervision-based evaluation of primary healthcare workers’ performance after mhGAP-HIG training in Pakistan

Background: The province of Khyber Pakhtunkhwa (KP) in Pakistan faces significant gaps in mental health services, marked by limited resources and inequitable distribution of services. To strengthen the existing services in nine districts of the province, 105 primary healthcare workers (PHCWs)— including primary care physicians and clinical psychologists—were previously trained to assess and manage mental health conditions using mhGAP-HIG adapted for Pakistan. The PHCWs received remote supervision for three months post-training. This study discusses the mechanism for remote supervision using digital technology and evaluated its impact on the performance of trained PHCWs. Methods: A mixed-method approach was used to analyze clinical the reported data. The assessment, management (including pharmacological and psychosocial interventions), and referral needs in all reported cases were monitored during supervision. Both qualitative and quantitative feedback from the PHCWs were also analyzed. Results: Out of 105 trained PHCWs, 50.34 % submitted 413 cases through the mhGAP-HIG-PK mobile application. Supervision was crucial in ensuring compliance with assessment protocols in 24.70 % cases, management protocols in 38.25 % of cases and referral protocols in 5 % of cases. The most frequently identified condition was depressive disorder (56.9 %). Commonly reported stressors by the patients at the primary healthcare included bereavement, socio-economic difficulties, marital challenges, and stressors related to family. PHCWs expressed a preference for remote supervision and found it beneficial for assessment (61.1 %), management (72.2 %), and referral (44.4 %). Conclusion: Following mhGAP training, remote supervision using digital tools can be effective in monitoring the performance of PHCWs to enhance their skills to assess and manage mental health conditions in low-resource settings. The collection and evaluation of supervision-based data are crucial for improving training programs to strengthen the capacity of PHCWs.