Teacher Nomination of School-aged Children for Mental Health Services in a Low and Middle Income Country

• Main Authors: Christina M. Cruz (Author), Molly M. Lamb (Author), Karen Hampanda (Author), Priscilla Giri (Author), Matthew Campbell (Author), Bijita Chowdhury (Author), Aileen A. Giardina (Author), Bradley N. Gaynes (Author), Michael Matergia (Author)
• Format: Book
• Published: Taylor & Francis Group, 2021-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1654-9880; 10.1080/16549716.2020.1861921
• Keywords: global mental health; child mental health; teacher identification; task-shifting; india; Public aspects of medicine; RA1-1270
• Source: Global Health Action, Vol 14, Iss 1 (2021)

Background: Knowledgeable in child development, primary school teachers in low- and middle-income countries (LMICs) have the potential to identify their students needing mental health care. Objective: We evaluated whether teachers in Darjeeling, India can accurately nominate school-aged children for mental health services after training and aided by a novel tool. Methods: In 2018, 19 primary school teachers from five low-cost private (LCP) schools in rural Darjeeling were trained to nominate children needing care. Teachers evaluated all of their students aided by a novel tool, 'Behavior Type and Severity Tool' (BTST), completed the Achenbach Teacher Report Form (TRF) as a mental health status reference standard, and nominated two students for care. Sensitivity and specificity of being nominated compared to TRF overall and subdomain scores were calculated. BTST performance was determined by comparing BTST and TRF scores and creating Receiver Operating Characteristic curves to determine optimal cutoffs. Multivariable regression models were used to identify demographic predictors of teacher accuracy using the BTST. Results: For students demonstrating a clinical or borderline score in at least one TRF subdomain, the sensitivity (72%) and specificity (62%) of teacher nomination were moderately high. BTST overall scores and TRF Total Problem scores were correlated (Spearman's ρ = 0.34, p < 0.0001), as were all subdomains. For the TRF Total Problem score, a maximum Youden's J of 0.39 occurred at BTST cutoff >4 for borderline struggles and 0.54 at the BTST cutoff >6 for clinical struggles. Younger teacher age, less education, less formal education training, and more years of experience were positively associated with teacher accuracy. Conclusions: With training and a simple decision support tool, primary school teachers in an LMIC nominated students for mental health services with moderate accuracy. With the BTST being weakly accurate, teachers' judgment largely accounted for the moderate accuracy of nominations.