Supervision equivalence

This paper presents a general framework for modular synthesis of supervisors for discrete event systems. The approach is based on compositional minimisation, using concepts of process equivalence. Its result is a compact representation of a least restrictive supervisor that ensures controllability and nonblocking. The method is demonstrated to reduce the number of states to be constructed for a simple manufacturing example, and the framework is proven to be sound

Lesbian, Gay, and Bisexual Supervisees’ Experiences of LGB-Affirmative and Nonaffirmative Supervision

Lesbian, gay, and bisexual (LGB) supervisees were interviewed regarding their experiences of LGB affirmative and nonaffirmative supervision. Supervisees were asked to describe one of each type of event (i.e., affirmative, nonaffirmative) from their past supervision. In LGB-affirmative supervision, all supervisees felt supported in their LGB-affirmative work with clients. Supervisees perceived that the affirming events also positively affected the supervision relationship, client outcomes, and themselves as supervisees. In LGB nonaffirming supervision, supervisees perceived supervisors to be biased or oppressive toward supervisees’ clients or themselves on the basis of LGB concerns or identity. From supervisees’ perspectives, the nonaffirming events negatively affected the supervision relationship, client outcomes, and supervisees. Implications for research and supervision are discussed

School Counseling Site Supervisor Training: An Exploratory Study

This study explored the supervision training needs of site supervisors of master’s program school counseling interns via the construct of selfefficacy. Using the Site Supervisor Self-Efficacy Survey developed for this study, the authors surveyed school counseling site supervisors in the states of Oregon and Washington (N = 147) regarding their hours of supervision training and their supervisor self-efficacy. Results indicated that 54% of school counseling site supervisors had little or no counseling supervision training. Supervisor self-efficacy appeared to be relatively strong, consistently so for school counseling site supervisors with over 40 hours of supervision training. A partial correlation indicated a slightly positive relationship between the hours of supervision training received and perceived self-efficacy regarding supervision. Implications regarding school counseling site supervisor training and future research are offered

Does reflective supervision have a future in English local authority child and family social work?

Purpose – (1) to discuss the underlying assumption that social workers need reflective supervision specifically, as opposed to managerial or any other form of supervision or support; and (2) to consider whether our focus on the provision of reflective supervision may be preventing us from thinking more broadly and creatively about what support local authority child and family social workers need and how best to provide it. Methodology/approach – Argument based on own research and selective review of the literature Findings – Reflective supervision has no future in local authority child and family social work because (1) there is no clear understanding of what reflective supervision is, (2) there is no clear evidence for is effectiveness, and (3) a sizeable proportion of local authority child and family social workers in England do not receive reflective supervision and many never have. Originality/value – Challenges the received wisdom about the value of reflective supervision and advocates exploring alternative models for supporting best practice in child and family social work

Survey of community banks in the Tenth Federal Reserve District (survey responses)

Complete survey with statistical summaries of responsesCommunity banks ; Federal Reserve District, 10th

Conducting Successful Supervision: Novel Elements Towards an Integrative Approach

In recent years that has been an increasing interest in supervision within the UK's cognitive behaviour therapy (CBT) community. This is because the role of supervision has begun to be recognized in relation to the delivery of effective clinical services (Department of Health, 1998), and because of a clear recognition of the need to ensure that CBT practitioners are competent. Perhaps less well recognized in CBT are a number of interesting educational approaches to supervision, ones that may make supervision more successful. This paper summarizes some of these theories from a CBT perspective. Whilst the evidence base does not yet justify being too prescriptive, it is argued that some of these theories, such as Vygotsky's notion of the “Zone of Proximal Development”, provide helpful prompts for reflecting on CBT supervision. An integrative model is constructed from these theories, with illustrative examples and suggestions for future research

Hierarchical agent supervision

Agent supervision is a form of control/customization where a supervisor restricts the behavior of an agent to enforce certain requirements, while leaving the agent as much autonomy as possible. To facilitate supervision, it is often of interest to consider hierarchical models where a high level abstracts over low-level behavior details. We study hierarchical agent supervision in the context of the situation calculus and the ConGolog agent programming language, where we have a rich first-order representation of the agent state. We define the constraints that ensure that the controllability of in-dividual actions at the high level in fact captures the controllability of their implementation at the low level. On the basis of this, we show that we can obtain the maximally permissive supervisor by first considering only the high-level model and obtaining a high- level supervisor and then refining its actions locally, thus greatly simplifying the supervisor synthesis task

Supervisors\u27 Reports of the Effects of Supervisor Self-Disclosure on Supervisees

Using consensual qualitative research, researchers interviewed 16 supervisors regarding their use of self-disclosure in supervision. Supervisors reported that their prior training in supervisor self-disclosure (SRSD) came via didactic sources and encouraged judicious use of SRSD. Supervisors used SRSD to enhance supervisee development and normalize their experiences; supervisors did not use SRSD when it derailed supervision or was developmentally inappropriate for supervisees. In describing specific examples of the intervention, SRSD occurred in good supervision relationships, was stimulated by supervisees struggling, was intended to teach or normalize, and focused on supervisors\u27 reactions to their own or their supervisees\u27 clients. SRSD yielded largely positive effects on supervisors, supervisees, the supervision relationship, and supervisors\u27 supervision of others

Training Complex Models with Multi-Task Weak Supervision

As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown accuracies, may output correlated labels, and may label different tasks or apply at different levels of granularity. We propose a framework for integrating and modeling such weak supervision sources by viewing them as labeling different related sub-tasks of a problem, which we refer to as the multi-task weak supervision setting. We show that by solving a matrix completion-style problem, we can recover the accuracies of these multi-task sources given their dependency structure, but without any labeled data, leading to higher-quality supervision for training an end model. Theoretically, we show that the generalization error of models trained with this approach improves with the number of unlabeled data points, and characterize the scaling with respect to the task and dependency structures. On three fine-grained classification problems, we show that our approach leads to average gains of 20.2 points in accuracy over a traditional supervised approach, 6.8 points over a majority vote baseline, and 4.1 points over a previously proposed weak supervision method that models tasks separately