Managing clients’ expectations at the outset of online Cognitive Behavioural Therapy (CBT) for depression

Background Engaging clients in psychotherapy by managing their expectations is important for therapeutic success. Initial moments in first sessions of therapy are thought to afford an opportunity to establish a shared understanding of how therapy will proceed. However there is little evidence from analysis of actual sessions of therapy to support this. Objective This study utilised recordings to examine how therapists manage clients’ expectations during the first two sessions of online Cognitive Behavioural Therapy (CBT). Methods Expectation management was investigated through conversation analysis of sessions from 176 client-therapist dyads involved in online CBT. The primary focus of analysis was expectation management during the initial moments of first sessions, with a secondary focus on expectations at subsequent points. Analysis Clients’ expectations for therapy were most commonly managed during the initial moments of first sessions of therapy. At this point, most therapists either outlined the tasks of the first and subsequent sessions (n=36), or the first session only (n=108). On other occasions (n = 32), no attempt was made to manage clients’ expectations by outlining what would happen in therapy. Observations of the interactional consequences of such an absence suggest clients may struggle to engage with the therapeutic process in the absence of appropriate expectation management by therapists. Conclusion Clients may more readily engage from the outset of therapy when provided with an explanation that manages their expectation of what is involved. Therapists can accomplish this by projecting how therapy will proceed, particularly beyond the initial session.

The contribution of sport and performance psychology to coaching

The role of the sport and performance psychologists offers an historical base to many of the conceptualizations, strategies and tools currently utilized by coaches and coaching psychologists. The world of elite sport and performance offers little room for negativity, little tolerance for cognitions, emotions and behaviours that are less than optimal; in an environment that sets high expectation, makes obvious comparisons and is driven by competition; all within inflexible timelines and with harsh consequences for those that don’t make the grade. From this context the presenters will reflect on their development, challenges and expertise; offering ideas and tools that translate into the coaching arena.

Future perfect

"Future Perfect" is a solo artist exhibition featuring a 9 channel video installation, which is comprised of looped computer animation projections. In the first room, the big one, there are nine projections of looped computer animations. Many of these look like representations of gallery spaces containing sculptures, including rotating interpenetrating discs, bouncing coloured coffins, and jostling cardboard cubes (the cubes are blank, then covered in drawings, then covered in photographic imagery). In one video, a man and a woman walk towards one another but never get together. In the second room, an animated video on a flatscreen suggests an origin story. The subtitles tell how, in Russia, my great-grandfather made a joke about Stalin's child bride that cost him his life. That one isn’t a loop; it has a beginning, middle, and end. Lying on the floor, in front of the video, are two slightly crumpled mural prints of photographs of the ocean. There's also a clear Perspex cloud shape on a wall. Viewers will see themselves reflected in it, as if it were a distant hovering mirage. The first room of the exhibition, where objects are set in perpetual motion, is about departure. The second room registers some sense of arrival. The future perfect implies looking back on something that hasn't happened yet; future and past are conflated and the present is somehow deferred. The future perfect combines anticipation and reflection, and it relates to my interest in combining 3-D animation with other mediums like drawing, painting, and shot video. In my work, the virtual and actual coexist in tension, just like experience and expectation in the future perfect.

Case Study 4: Education Doctoral Student - "An Exploratory Study of Online Social Networking within a Doctorate of Education Program"

Introduction The professional doctorate is specifically designed for professionals investigating real-world problems and relevant issues for a profession, industry, and/or the community. The focus is scholarly research into professional practices. The research programme bridges academia and the professions, and offers doctoral candidates the opportunity to investigate issues relevant to their own practices and to apply these understandings to their professional contexts. The study on which this article is based sought to track the scholarly skill development of a cohort of professional doctoral students who commenced the course in January 2008 at an Australian university. Because they hold positions of responsibility and are time-poor, many doctoral students have difficulty transitioning from professional practitioner to researcher and scholar. The struggle many experience is in the development of a theoretical or conceptual standpoint for argumentation (Lesham, 2007; Weese et al., 1999). It was thought that the use of a scaffolded learning environment that drew upon a blended learning approach incorporating face to face intensive blocks and collaborative knowledge-building tools such as wikis would provide a data source for understanding the development of scholarly skills. Wikis, weblogs and similar social networking software have the potential to support communities to share, learn, create and collaborate. The development of a wiki page by each candidate in the 2008 cohort was encouraged to provide the participants and the teaching team members with textual indicators of progress. Learning tasks were scaffolded with the expectation that the candidates would complete these tasks via the wikis. The expectation was that cohort members would comment on each other’s work, together with the supervisor and/or teaching team member who was allocated to each candidate. The supervisor is responsible for supervising the candidate’s work through to submission of the thesis for examination and the teaching team member provides support to both the supervisor and the candidate through to confirmation. This paper reports on the learning journey of a cohort of doctoral students during the first seven months of their professional doctoral programme to determine if there had been any qualitative shifts in understandings, expectations and perceptions regarding their developing knowledge and skills. The paper is grounded in the literature pertaining to doctoral studies and examines the structure of the professional doctoral programme. Following this is a discussion of the qualitative study that helped to unearth key themes regarding the participants’ learning journey.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Mind the insolvency gap : lessons to be learned from audit expectations gap theory

There is a general perception that public confidence in the insolvency profession is low as the result of the recent unethical practices of a few high profile liquidators. As a result, the effectiveness of the current regulatory mechanisms has been questioned, leading to a review of the performance of insolvency practitioners and subsequent regulation proposals. The challenge for the insolvency profession is balancing the expectations of the general public whilst ensuring that the obligations and duties imposed upon them are performed to acceptable and realistic standards. It is difficult (if not impossible) for the profession to meet this challenge in the absence of a cohesive framework which identifies those issues that require further regulation as opposed to those that relate to general education on the insolvency process. This paper will examine the audit expectations gap theory in the context of insolvency practitioners and suggests that a model based on this theory provides an effective framework for evaluating the regulation of the insolvency industry.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Error and variance bounds on sigmoidal neurons with weight and input errors

Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.

Development of fourth-grade students’ understanding of experimental and theoretical probability

Students explored variation and expectation in a probability activity at the end of the first year of a 3-year longitudinal study across grades 4-6. The activity involved experiments in tossing coins both manually and with simulation using the graphing software, TinkerPlots. Initial responses indicated that the students were aware of uncertainty, although an understanding of chance concepts appeared limited. Predicting outcomes of 10 tosses reflected an intuitive notion of equiprobability, with little awareness of variation. Understanding the relationship between experimental and theoretical probability did not emerge until multiple outcomes and representations were generated with the software.

Teaching reflection for service-learning

In this chapter, we discuss an approach for teaching pre-service teachers how to critically reflect on their experiences in a Service-learning program in an advanced subject about inclusive education. The approach was informed by critical social theory, with the expectation that students would engage in transformational learning. By explicitly teaching the students to engage in critical reflective thinking (Fishbowl discussions) and examine the depth of their critical reflection against a heuristic (the 4Rs reflection framework), the final-year Bachelor of Education students were able to gain a deeper understanding of the subject and experience transformational learning. We provide contextual information about the Service-learning program and discuss critical social theory for transformational learning, as well as how the teaching team taught critical reflection. Based on the evidence gathered from the students, we consider lessons learned by the teaching team and provide recommendations for teaching reflection in Service-learning programs.

‘Works well with others’ : examining the different types of small group learning approaches and their implications for law student learning outcomes

In the current regulatory climate, there is increasing expectation that law schools will be able to demonstrate students’ acquisition of learning outcomes regarding collaboration skills. We argue that this is best achieved through a stepped and structured whole-of-curriculum approach to small group learning. ‘Group work’ provides deep learning and opportunities to develop professional skills, but these benefits are not always realised for law students. An issue is that what is meant by ‘group work’ is not always clear, resulting in a learning regime that may not support the attainment of desired outcomes. This paper describes different types of ‘group work', each associated with distinct learning outcomes. It suggests that ‘group work’ as an umbrella term to describe these types is confusing, as it provides little indication to students and teachers of the type of learning that is valued and is expected to take place. ‘Small group learning’ is a preferable general descriptor. Identifying different types of small group learning allows law schools to develop and demonstrate a scaffolded, sequential and incremental approach to fostering law students’ collaboration skills. To support learning and the acquisition of higherorder skills, different types of small group learning are more appropriate at certain stages of the program. This structured approach is consistent with social cognitive theory, which suggests that with the guidance of a supportive teacher, students can develop skills and confidence in one type of activity which then enhances motivation to participate in another.

Reducing recidivism by first time drink driving offenders

This research provides a detailed description of first time drink driving offenders at the time of their court appearance and at follow-up to examine the factors leading to subsequent drink driving. To develop models for behavioural change a novel theoretical application of the Health Action Process Approach was used to determine what enables some offenders to avoid future drink driving. Utilising self-report and official offence records in the follow-up of offenders enabled an in depth exploration of first offender characteristics and drink driving behaviour. The research demonstrates that first offenders are not a homogenous group in terms of their characteristics or the circumstances of the offence and will be used to develop tailored countermeasures for first offenders including online intervention programs.

Injecting Creative Commons Licensing into university policy and publishing

Australian universities are publishing previously unpublished works such as theses in institutional repositories or by publishing scholarly journals on their own presses. This re-invented role of publisher is due in part, to the availability of digital age technologies which scaffold the publishing process and facilitate inexpensive production of digital-only journals. The global push for Open Access to the outputs of publicly-funded research has also been a major driver. Research funder mandates and institutional Open Access policies apply only to publications for which the authors have no expectation of commercial gain. In all cases the primary motivation is to disseminate widely for maximum uptake with attribution to the author thereby increasing impact. This makes works published in institutional repositories and on university presses ideal candidates for Creative Commons licences.

Introducing telemetrics

This paper introduces a new methodology for analyzing and measuring engagement with television content by users of Twitter. Drawing on factors such as the network, viewing audience, and date of broadcast to establish a baseline expectation for volume of tweets around a television show, and applying techniques from the field of sabermetrics to create neutral volume figures (‘weighted tweets’) which exclude these variables, our metrics provide new insights into television’s social media presence. The methodology provides a variety of new measures for analysing the social media strategies of individual television programs, channels and networks, for comparing users’ engagement with programs, channels or networks, and for predicting future volumes of tweets.