Detecting pattern transitions in psychological time series - A validation study on the Pattern Transition Detection Algorithm (PTDA).

With the increasing use of real-time monitoring procedures in clinical practice, psychological time series become available to researchers and practitioners. An important interest concerns the identification of pattern transitions which are characteristic features of psychotherapeutic change. Change Point Analysis (CPA) is an established method to identify the point where the mean and/or variance of a time series change, but changes of other and more complex features cannot be detected by this method.

Differences by sexual orientation in treatment outcome and satisfaction with treatment among inpatients of a German psychiatric clinic.

A wealth of research suggests that sexual minority individuals experience stigma and lack of sexual minority specific competencies in mental health care, which could lead to less optimal treatment outcome. However, most related research suffers from methodological limitations, such as selected samples, retrospective design, or not assessing treatment outcome. To overcome some of these limitations, we explored if sexual minority patients have poorer treatment outcome and are less satisfied with treatment in a mental health care setting not specialized in sexual minority issues.

Convergent Validation of Methods for the Identification of Psychotherapeutic Phase Transitions in Time Series of Empirical and Model Systems.

Aim: In many cases, the dynamics of psychotherapeutic change processes is characterized by sudden and critical transitions. In theoretical terms, these transitions may be "phase transitions" of self-organizing nonlinear systems. Meanwhile, a variety of methods is available to identify phase transitions even in short time series.