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教师提名学龄儿童接受心理健康服务:来自中低收入国家的证据。

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

机构信息

Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine , Chapel Hill, NC, USA.

Department of Epidemiology, Colorado School of Public Health , Aurora, CO, USA.

出版信息

Glob Health Action. 2021 Jan 1;14(1):1861921. doi: 10.1080/16549716.2020.1861921.

Abstract

: 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. : 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. : 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. : 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. : 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.

摘要

: 了解儿童发展的小学教师在中低收入国家(LMICs)有潜力识别需要心理健康护理的学生。: 我们评估了印度大吉岭的教师在经过培训并借助新工具的帮助后,是否能够准确地提名需要心理健康服务的学龄儿童。: 2018 年,来自大吉岭农村地区五所低成本私立(LCP)学校的 19 名小学教师接受了提名需要照顾的儿童的培训。教师借助一种新工具“行为类型和严重程度工具”(BTST)评估了他们所有的学生,完成了 Achenbach 教师报告表(TRF)作为心理健康状况参考标准,并提名了两名学生接受护理。与 TRF 总分和子域得分相比,被提名的敏感性和特异性。BTST 性能通过比较 BTST 和 TRF 得分以及创建接收器操作特征曲线来确定最佳截止值来确定。使用多变量回归模型,使用 BTST 识别教师准确性的人口统计学预测因素。: 对于在至少一个 TRF 子域中表现出临床或边界分数的学生,教师提名的敏感性(72%)和特异性(62%)为中度高。BTST 总分和 TRF 总问题得分呈正相关(Spearman's ρ=0.34,p <0.0001),所有子域也呈正相关。对于 TRF 总问题得分,BTST 截止值>4 时,边界挣扎的最大 Youden J 为 0.39,BTST 截止值>6 时,临床挣扎的最大 Youden J 为 0.54。年轻的教师年龄、较少的教育、较少的正规教育培训和更多的工作经验与教师准确性呈正相关。: 经过培训和简单的决策支持工具,中低收入国家的小学教师以中等准确性提名学生接受心理健康服务。由于 BTST 准确性较弱,教师的判断在很大程度上解释了提名的中等准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5194/7894443/e89a64b45b65/ZGHA_A_1861921_F0001_OC.jpg

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