Natural Systems Thinking and the Human Family

How Do Anxiety and Relationship Factors Influence the Application of Childbirth Education Strategies During Labor and Birth: A Bowen Family Systems Perspective

The effectiveness of childbirth education (CBE) has long been debated with studies showing contradictory outcomes for mothers and babies. Understanding how what is learned in CBE is translated into practice during labor and birth is an area that requires investigation as this may be a mediating factor in its effectiveness. Bowen family system theory's concept of differentiation of self, the ability to be guided by and to act from one's beliefs and values, is an organizing principle that may affect how relational factors affect the use and application of CBE at the time of birth. The ability to act with emotional maturity when faced with a stressor, such as childbirth, depends on an individual's capability to separate thoughts from the more reactive feeling process. Recognizing how one's level of differentiation interacts with the anxious responses of others may assist pregnant women and birth partners to make decisions more objectively about how they want to manage the birthing process. For the health professional, understanding the interplay of relationship variables, physiological stress, anxiety and individual reactivity may allow for the provision of more thoughtful evidence-based practice, which may increase objectivity, and aid communication and decision-making for women during birth. Bowen theory, as a comprehensive systems-based approach to understanding human functioning under stress, offers a novel approach to exploring the application of CBE during birth.

Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG

Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect.