Reviewing the Social GRACES: What Do They Add and Limit in Systemic Thinking and Practice?

The Social Graces framework is increasingly used within training institutions, as a means of encouraging learners to critically explore issues of social difference. Attending to issues of power and diversity is believed to help trainee family therapists become more alert to any biases that may impact on therapy. This review paper draws on the developing literature to examine what the social graces add and limit in systemic thinking and practice. It also considers an alternative approach for exploring power and difference in family therapy practice

Staff experiences of significant moments in systemic team formulation on adult inpatient mental health wards.

Although studies have found potentially positive benefits and challenges of team formulation practice, what the research field is currently lacking is context around why team formulations are seemingly having this impact. In this practice-based research, I aimed to examine my practice of systemic team formulation, and to explore what moments team members experienced as being transformative and/or significant in the process of systemic team formulation. I conducted focus group interviews with staff at an acute inpatient mental health service, where I work, to explore this topic. I analysed the data using an interpretive phenomenological analysis framework for focus groups (Palmer et al., 2010) with some systemic adaptations of my own. There was some overlap of findings with regards to the benefits and challenges of team formulation cited in other studies, but there were also some novel findings about what staff members found to be significant in the process of systemic team formulation, such as giving team members permission to think systemically and relationally about client systems and themselves. There were also themes around some of the dominant discourses in acute inpatient mental health wards which systemic team formulation, perhaps inadvertently, challenges. These findings may give insight into the key change moments for teams in the process of systemic team formulation, and may have tentative implications for the practice of team formulation more broadly, and for the practice, training, and supervision of systemically influenced team formulation practitioners. Key words: Systemic team formulation, team formulation, acute inpatient mental health, change process research, systemic therapy, interpretive systemic phenomenological analysi

Engaging a family’s support network in non‐violent resistance: the experiences of supporters

Non-violent resistance (NVR) is a systemic approach which aims to build parent agency and a positive support network around children who display destructive behaviour. Supporters play a key role in NVR, but there is limited research on how to engage them in NVR. The aim of this study was to explore the experiences of the members of a family’s support network on the implementation of NVR. These experiences were then interpreted in line with theories of motivation and change in order to understand how the NVR practitioner and parents can best engage with supporters in future. Eight supporters were interviewed and themes developed using interpretative phenomenological analysis. The study analysis found five themes: contemplation, committing to action, looking for change, needing to cope and working within a social context. These are interpreted in line with theories of change and motivation. Clinical recommendations and recommendations for further research are discussed. Practitioner points: Practitioners need to consider that taking part in NVR can be experienced by supporters in both positive and negative ways Practitioners may improve supporters’ engagement and motivation in the process by considering areas such as understanding NVR, positive communication and impact on relationships.</p

Improving Family Engagement in an Adult Inpatient Mental Health Service using an Action Research Framework

Family involvement in mental health treatment has been shown to improve outcomes for service users. This project used a whole system-focused action research framework and involved service users, family members and healthcare professionals to develop ways to improve family engagement in an adult inpatient mental health service. Focus groups were conducted with two service users, two family members and four healthcare professionals to discuss their experiences of family involvement and develop initial ideas. A problem-solving group, involving service users, family members and professionals, was used to develop the solutions. The project identified context-specific solutions to improve family engagement, which included specific training for professionals working on the inpatient wards, questionnaires to facilitate conversations with families, and sharing information about mental health and the inpatient mental health service with families. These may be helpful for other adult inpatient services.</p

LAPNet:Non-rigid Registration derived in k-space for Magnetic Resonance Imaging

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors