Gardner William, Kelleher Kelly J, Pajer Kathleen A
University of Pittsburgh School of Medicine, Pennsylvania, USA.
Med Care. 2002 Sep;40(9):812-23. doi: 10.1097/00005650-200209000-00010.
Efficient and accurate instruments for assessing child psychopathology are increasingly important in clinical practice and research. For example, screening in primary care settings can identify children and adolescents with disorders that may otherwise go undetected. However, primary care offices are notorious for the brevity of visits and screening must not burden patients or staff with long questionnaires. One solution is to shorten assessment instruments, but dropping questions typically makes an instrument less accurate. An alternative is adaptive testing, in which a computer selects the items to be asked of a patient based on the patient's previous responses. This research used a simulation to test a child mental health screen based on this technology.
Using half of a large sample of data, a computerized version was developed of the Pediatric Symptom Checklist (PSC), a parental-report psychosocial problem screen. With the unused data, a simulation was conducted to determine whether the Adaptive PSC can reproduce the results of the full PSC with greater efficiency.
PSCs were completed by parents on 21,150 children seen in a national sample of primary care practices.
Four latent psychosocial problem dimensions were identified through factor analysis: internalizing problems, externalizing problems, attention problems, and school problems. A simulated adaptive test measuring these traits asked an average of 11.6 questions per patient, and asked five or fewer questions for 49% of the sample. There was high agreement between the adaptive test and the full (35-item) PSC: only 1.3% of screening decisions were discordant (kappa = 0.93). This agreement was higher than that obtained using a comparable length (12-item) short-form PSC (3.2% of decisions discordant; kappa = 0.84).
Multidimensional adaptive testing may be an accurate and efficient technology for screening for mental health problems in primary care settings.
在临床实践和研究中,高效且准确的儿童精神病理学评估工具愈发重要。例如,在基层医疗环境中进行筛查能够识别出患有某些疾病的儿童和青少年,否则这些疾病可能会未被发现。然而,基层医疗诊所的就诊时间短暂是出了名的,筛查绝不能因冗长的问卷给患者或工作人员带来负担。一种解决办法是缩短评估工具,但删减问题通常会使工具的准确性降低。另一种方法是自适应测试,即计算机根据患者先前的回答选择要问患者的问题。本研究使用模拟来测试基于该技术的儿童心理健康筛查。
利用大量数据样本的一半,开发了儿童症状清单(PSC)的计算机化版本,这是一种由家长报告的心理社会问题筛查工具。利用未使用的数据进行模拟,以确定自适应PSC是否能更高效地重现完整PSC的结果。
在全国基层医疗实践样本中,21150名儿童的家长完成了PSC。
通过因子分析确定了四个潜在的心理社会问题维度:内化问题、外化问题、注意力问题和学校问题。一项测量这些特征的模拟自适应测试平均每位患者问11.6个问题,49%的样本被问到的问题为五个或更少。自适应测试与完整的(35项)PSC之间具有高度一致性:只有1.3%的筛查决策不一致(kappa = 0.93)。这种一致性高于使用类似长度(12项)简版PSC所获得的一致性(3.2%的决策不一致;kappa = 0.84)。
多维自适应测试可能是在基层医疗环境中筛查心理健康问题的一种准确且高效的技术。