Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Prevention and intervention of post-traumatic stress disorder (PTSD) in children is an important issue of the healthy China strategy. The world health organization (WHO) predicted that global burden of child injury will continue to rise, especially in low and middle-income countries. It is estimated that 10% to 20% of children after traumatic events will experience post-traumatic stress disorder symptoms such as warning, evasion, and negative emotions. PTSD confer a heavy long term burden of disease among children. And Child PTSD was indicated to have a long tail effect, as great heterogeneity exists among children with PTSD regarding their following health outcomes. In addition, post-traumatic stress disorder in children is highly correlated with behavior problems, depression, substance abuse, crime, and suicide, and its negative effects can last into adulthood and even have intergenerational transmission effects. Current studies on the developmental outcome of PTSD in children mostly focus on whether PTSD symptoms decrease, increase or persist in children, while ignoring the types, mechanisms of prognosis, and intervention strategies of Child PTSD. Specifically, the types, characteristics, and mechanism of the developmental outcome of PTSD (transforming into physical and mental health, behavioral health, and other problems) have rarely been systematically explored, especially in the Chinses cultural context. For the reduction in the occurrence and development of PTSD, the trauma-focused cognitive behavioral intervention was proved to be rather effective in reducing the level of PTSD. However, there is not enough evidence from large-sample-sized, long-term randomized controlled studies based on school settings, and a shortage of targeted interventions for different stages of PTSD development. Therefore, it is of great academic value and practical significance to clarify the pathway of the occurrence and trajectory of PTSD in children, provide joint intervention programs based on the school setting, and reduce the risk of their transition to other physical and mental diseases. Moreover, based on a school-based teacher-children-parents cooperation framework, we would provide targeted intervention services to reduce the risk of Child PTSD, further shed light on individual-centered care in clinical practice. The purpose of this study is to conduct a follow-up investigation and quasi-experimental study on children and adolescents, focusing on: (1) the developmental trajectory and outcome types of children with PTSD. (2) What is the mechanism of prognosis for PTSD among children? (3) Whether school-based intervention services can promote the recovery of children with PTSD and reduce the risk of their transition to other physical and mental disorders? (4) According to the characteristics of Chinese children, how to develop early health service plans focusing on reducing traumatic events and promoting the recovery ability of children with PTSD? This study will identify the long-term trajectory types and mechanisms of Child PTSD prognosis in China, reduce the risk of children's PTSD to other physical and mental diseases through the joint intervention program based on the school setting, verify the effect of comprehensive intervention services on promoting the recovery of children's PTSD, and provide evidence for children's PTSD intervention and personalized diagnosis and treatment. Simultaneously, multiple theories were integrated to build the early-warning model, to interpret prognosis mechanism and comprehensive intervention strategy. Based on the "long tail theory", the early-warning model explore the trajectory and trait of the PTSD children. The prognosis mechanism of Child PTSD, incorporated the basic essence of the stress model and the resilience model, will reveal the developmental mechanism of Child PTSD. The process-based intergrated intervention strategy synthesized the characteristics of the long term effect, psychological environmental mechanism and task-shifting model. And this strategy could provide theoretical guidance for the school-based interventions of Child PTSD in the Chinese context.#posttraumatic stress disorder, long tail effect, mechanism of prognosis, intervention strategy
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Post-traumatic stress disorder (PTSD) could have negative effects on the development of children, and its impact can even last into adulthood. The traditional method to identify and diagnose PTSD in children is for the clinician or researcher to compares the symptoms with the criteria in the diagnostic manual. Therefore, the child who meets the symptom criteria could be diagnosed with PTSD. In addition, risk factors for PTSD in children are identified by traditional multiple regression methods using hypotheses based on previous literature or experience. However, these methods rely on clinicians or researchers' personal experience greatly. Moreover, identifying child PTSD is subjective, and the selected statistical method could impact the predicted risk factors. Generally, researchers use prediction method based on regression models. However, the identification of risk factors is not comprehensive enough, which needs a lot of empirical data to discuss. Thus, machine learning, as an emerging method to deal with big data, is a data-driven method to summarize rules and features based on existing data. Through continuous data training, the program could make its own judgment on whether children in new data have PTSD, which is more objective, faster and more efficient than human diagnosis. When predicting risk factors, machine learning models have also developed from traditional decision trees and regression to the field of deep learning, with greatly improves the accuracy of diagnosis and simultaneous processing of multi-dimensional variables. Therefore, using machine learning to predict children's PTSD could make up for the disadvantages of traditional prognosis investigation, which is difficult to follow for a long time and has large missing values, etc. Machine learning may also better solve other related problems, such as failing to detect PTSD symptoms in time and missing the optimal healing period due to the late-onset of PTSD in children. The application of machine learning in predicting the outcome of children’s PTSD results could be divided into two methods. One is the "classification" of supervised learning, which is the possible classification result of the artificially set training data sets. The other is "clustering", that is, the data in the training set would be automatically divided into several groups based on characteristics or some potential concepts. Each group is called a cluster, and then the commonalities of these clusters are artificially summarized through unsupervised learning. Although machine learning has some advantages in the diagnosis and recognition of PTSD in children, its application is still in the initial stage, with opportunities and challenges coexisting. It is worth noting that machine learning also has limitations such as a single algorithm, limited accuracy of prediction, different prediction results based on different models, relatively insufficient research on treatment methods, and difficulty in collecting children's PTSD indicators. In the future, researchers need to further improve the accuracy of machine learning diagnosis and children’s PTSD recognition, and explore more combinations of machine learning and traditional diagnosis methods. With the development of the Chinese medical industry, machine learning has shown great potential in the field of psychiatry. It is believed that the practical applications of machine learning in children’s PTSD would be developed rapidly in the future, which could provide guidance and suggestions for the early prevention or treatment of children’s PTSD.
Subjects: Psychology >> Applied Psychology submitted time 2022-03-26
Abstract：
Child PTSD was indicated to have a long tail effect, as great heterogeneity exists among children with PTSD regarding their following health outcomes. Though heavy burden from Child PTSD was found on individuals, families, and society, a few studies have systematically examined the types and mechanisms of prognosis of Child PTSD in a Chinese context, let alone specific intervention strategies. This study aims to conduct a longitudinal survey and quasi-experimental intervention among students, and tries to identify the long-term trajectory types and mechanisms of Child PTSD prognosis in China. Moreover, based on a school-based teacher-children-parents cooperation framework, we would provide targeted intervention services to reduce the risk of Child PTSD, further shed light on individual-centered care in clinical practice.
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Peer Review Status:
Awaiting Review
Subjects: Psychology >> Psychological Measurement submitted time 2021-11-15
Abstract： Abstract： Post-traumatic stress disorder (PTSD) has negative effects on children's development, even into adulthood. However, traditional diagnostic methods are difficult to quickly, objectively, and accurately identify and diagnose PTSD in children. Machine learning, as an emerging method to deal with a large number of variables and data, has gradually been applied to the research of early prediction, recognition, and auxiliary diagnosis of PTSD in children. Machine learning, with its advantages in performance and algorithm, can be applied to the recognition and prognosis of PTSD in children. Compared with self-reported diagnosis, the process of identifying and diagnosing PTSD in children through machine learning has unique advantages of high efficiency, objective accuracy, and resource-saving. Machine learning also has limitations in terms of hardware cost, algorithm selection, and prediction accuracy. In the future, researchers need to further improve the accuracy of machine learning diagnosis and recognition of PTSD in children and combine machine learning algorithms with traditional diagnosis methods for more exploration and application.
Peer Review Status:Awaiting Review
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