Background: This study is a preliminary study on an acceptance and commitment therapy (ACT) program that mitigates destructive experiential avoidance (DEA) behaviors, including self-harm behavior and addiction; Methods: Twenty participants aged 15–25 years who had confirmed DEA behavior within the last month participated in a total of six sessions of ACT. Demographic characteristics, history of psychiatric illness, and TYPES and patterns of DEA behavior were confirmed in the baseline survey. The severity of clinical symptoms, frequency of DEA behavior and impulsivity, characteristics of experiential avoidance (EA) behavior, depression, and quality of life (QOL) were measured before and after the program for comparative statistical tests using the intention-to-treat method. Furthermore, the severity of clinical symptoms was evaluated after each program, along with the frequency of DEA behavior and trends in impulsivity, which were investigated based on the behavior log; Results: After the ACT program, both the frequency of DEA behavior and impulsivity and the severity of clinical symptoms, depression, and anxiety decreased significantly. Furthermore, among the EA characteristics, pain aversion, distraction and inhibition, and delayed behavior significantly improved. Moreover, the overall QOL, psychological and social relationships, and QOL regarding the environment also improved; Conclusions: The results of this feasibility study demonstrate the potential of the ACT program as an effective intervention in DEA behavior. The results of this study may be used as preliminary data for future large-scale randomized studies.

Psychotic symptoms are rarely concurrent with the clinical manifestations of depression. Additionally, whether psychotic major depression is a subtype of major depression or a clinical syndrome distinct from non-psychotic major depression remains controversial. Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, we developed a machine-learning-algorithm-based prediction model for concurrent psychotic symptoms in patients with depressive disorders. The advantages of machine learning algorithms include the easy identification of trends and patterns, handling of multi-dimensional and multi-faceted data, and wide application. Among 1171 patients with depressive disorders, those with psychotic symptoms were characterized by significantly higher rates of depressed mood, loss of interest and enjoyment, reduced energy and diminished activity, reduced self-esteem and self-confidence, ideas of guilt and unworthiness, psychomotor agitation or retardation, disturbed sleep, diminished appetite, and greater proportions of moderate and severe degrees of depression compared to patients without psychotic symptoms. The area under the curve was 0.823. The overall accuracy was 0.931 (95% confidence interval: 0.897–0.956). Severe depression (degree of depression) was the most important variable in the prediction model, followed by diminished appetite, subthreshold (degree of depression), ideas or acts of self-harm or suicide, outpatient status, age, psychomotor retardation or agitation, and others. In conclusion, the machine-learning-based model predicted concurrent psychotic symptoms in patients with major depression in connection with the “severity psychosis” hypothesis.

Based on Korean data from the Research on Asian Psychotropic Prescription Pattern for Bipolar Disorder, this study tried to present prescription patterns in biopolar disorder (BD) and its associated clinical features. Based on the information obtained from the study with structured questions, the tendency of prescription pattern was studied and analyzed. Polypharmacy was predominant, including simple polypharmacy in 51.1% and complex polypharmacy in 34.2% of patients. Subjects associated with simple or complex polypharmacy were significantly younger, had higher inpatient settings, a larger portion of onset with manic episode, a shorter duration of untreated illness, a shorter duration of current episode, were more overweight, used less antidepressants and used more anxiolytics. These findings can suggest higher polypharmacy rate in more severe BD and highlight the necessity of monitoring the weight of subjects with polypharmacy.

The ability to conduct research and utilize its findings has been a fundamental competency for medical students, often assessed through the observable research outcomes. This study aims to compare the research competency of medical students before, during, and after the COVID-19 pandemic to explore the future direction of research education in medical schools. We collected information on research outcomes submitted by medical students as graduation requirements during the pre-COVID-19, pandemic, and post-COVID-19 periods based on the duration of social distancing. The research outcomes were classified into two categories: publication of a paper and presentation at a conference. We compared variables such as authorship, type of journal or conference, and type of literature or presentation. The research outcomes submitted by all medical school graduates at their senior year from 2018 to 2023 were included in the analysis. The graduation requirements were usually achieved by publishing a paper (67.5%) with a faculty of clinical medicine (63.5%) in the fourth year (50.0%), regardless of COVID-19 pandemic. The findings highlighted the significant impact of the COVID-19 pandemic on the research outcomes of medical students. Post hoc analysis revealed that while the quality of published papers improved during the COVID-19 pandemic, there was a regression in author contributions in the post-pandemic period. The impact of the COVID-19 pandemic on medical students’ experiences with research is likely that the weakened student initiative observed in participating in and conducting research will persist, especially after the pandemic. Therefore, it is imperative to implement strategies to increase motivation and empower medical students to research.