Department of Psychology, University of Hawai‛i at Mānoa, 2530 Dole Street, Sakamaki C400, Honolulu, HI, 96822, USA.
Child and Adolescent Mental Health Division, Hawai'i Department of Health, 3627 Kilauea Ave., Room 101, Honolulu, HI, 96816, USA.
Adm Policy Ment Health. 2022 Sep;49(5):810-820. doi: 10.1007/s10488-022-01200-7. Epub 2022 Jun 13.
This study utilized latent profile analysis to categorize youth served by a public mental health setting into homogenous classes. Then, associations between class membership and meeting clinical criteria by the latest assessment were examined. Caregiver responses to the Ohio Scales, Short Form, Problem Severity Scale for 1090 youth completed at entry into this public mental health system were subjected to latent profile analysis. This method classifies youth into categories based on mental health problem profiles, in order to determine the degree to which these groupings are related to later mental health outcomes. The classification of youth cases that emerged was then used to predict clinical remission at or nearest end of treatment, including final Ohio Scales Problem Severity scores and a measure of day-to-day functioning, the Child and Adolescent Functional Assessment Scale (CAFAS). A four-class model was identified as best representing the data, reflecting a relatively low-risk class (63.3% of the sample), an internalizing class (23.2%), a delinquency class (8.8%), and a high-risk class (4.7%). Individuals in the internalizing and high-risk classes had lower likelihoods of achieving problem remission than those in the low-risk and delinquency classes at the time of their last completed Ohio Scales. Additionally, youth assigned to the delinquency and high-risk classes had lower likelihoods of reaching functional impairment remission than those in the internalizing and low-risk classes. Youth membership in a class based on initial problem scores can be utilized to predict clinical remission over the course of treatment in public mental health care. Such class-based predictions support other methods of predicting outcomes and can be used by clinicians to develop more informed treatment plans and to adjust treatment based on such classifications.
本研究利用潜在剖面分析将公共心理健康机构服务的青少年分为同质群体。然后，检查了群体成员资格与最新评估中符合临床标准之间的关联。对进入该公共心理健康系统的 1090 名青少年完成的俄亥俄量表、短形式、问题严重程度量表的照顾者反应进行了潜在剖面分析。这种方法根据心理健康问题概况对青少年进行分类，以确定这些分组与后来的心理健康结果的相关程度。然后，使用青年案件的分类来预测治疗结束时或最近的临床缓解，包括最终的俄亥俄量表问题严重程度评分和衡量日常功能的儿童和青少年功能评估量表（CAFAS）。确定了一个四类别模型，该模型最好地代表了数据，反映了一个相对低风险类别（样本的 63.3%）、内化类别（23.2%）、犯罪类别（8.8%）和高风险类别（4.7%）。与低风险和犯罪类别相比，内化和高风险类别的个体在完成俄亥俄量表的最后一次时，达到问题缓解的可能性较小。此外，与内化和低风险类别相比，被分配到犯罪和高风险类别的青少年达到功能障碍缓解的可能性较小。基于初始问题得分的群体成员资格可用于预测公共心理健康护理治疗过程中的临床缓解。基于群体的此类预测支持其他预测结果的方法，临床医生可以利用这些方法制定更明智的治疗计划，并根据这些分类调整治疗。
