Commentary on Three Articles on Self‐practice/Self‐reflection in Cognitive‐Behavioural Therapy

The commentary embraces three valuable contributions to the development of professional practice: (a) engagement with self‐practice/self‐reflection as a professional self‐development: the role of therapist beliefs; (b) spontaneous self‐practice of cognitive‐behavioural therapy (CBT) by aboriginal counsellors during and following CBT training: a retrospective analysis of the facilitating conditions and impact; (c) developing metacompetence in low intensity CBT interventions: evaluating a self‐practice/self‐reflection programme for experienced low intensity CBT practitioners. Each article provides different perspectives of the self‐practice and self‐reflection benefits for efficient therapy and their use as powerful resources in therapeutic training skills in general and CBT in particular. Conclusions of their contributions close the commentary.     

An evaluation of computer-based programmed instruction for promoting teachers' greetings of parents by name

Although greeting parents by name facilitates subsequent parent-teacher communication, baseline measures revealed that 4 preschool teachers never or rarely greeted parents by name during morning check-in. To promote frequent and accurate use of parents' names by teachers, the effects of a fully automated computerized assessment and programmed instruction (CAPI) intervention were evaluated in a multiple baseline design. The CAPI intervention involved assessment and training of relations among parents' and children's pictures and names, and produced rapid learning of parent names. The CAPI intervention also resulted in substantial improvements in the classroom use of parents' names for 3 of the 4 teachers; however, a supervisor-mediated feedback package (consisting of instructions, differential reinforcement, and error correction) was necessary to maintain name use for 2 of those teachers. The practical strengths and limitations of computer-based teacher training are discussed.     

Group process in Jungian analytic training and institute life

Group process experience for analytic candidates is a neglected dimension of training, and receives little attention in the analytic literature. Jung observed group dynamics, but he never studied them closely, attending instead to the psychology of the individual. Unconscious currents in small groups have been studied by others, most notably by Wilfred Bion, and there are similarities between his theories of the group unconscious and Jung's theories of complexes. Experiential and didactic seminars in group process were added to the analytic curriculum at the C.G. Jung Institute of San Francisco in the early 1990s, leading to changes in the group dynamic of trainees and analysts alike. A discussion of the theories of Bion and Jung are followed by a report on our experiences of facilitating group process for analytic candidates. We give quotes from candidates and analyst members to illustrate the group process and its effects. The need for further study to develop a uniquely Jungian perspective on the unconscious structure and dynamics of the group is suggested.     

A randomised worksite comparison of acceptance and commitment therapy and stress inoculation training

In this comparative intervention study, 107 working individuals with above average levels of distress were randomly assigned to one of three conditions: acceptance and commitment therapy (ACT; n = 37); stress inoculation training (SIT; n = 37); or a waitlist control group (n = 33). The interventions were delivered to small groups in the workplace via two half-day training sessions. ACT and SIT were found to be equally effective in reducing psychological distress across a three month assessment period. Mediation analysis indicated that the beneficial impact of ACT on mental health resulted from an increase in psychological flexibility rather than from a change in dysfunctional cognitive content. Contrary to hypothesis, a reduction in dysfunctional cognitions did not mediate change in the SIT condition. Results suggest that the worksite may offer a useful, yet underutilised, arena for testing cognitive-behavioural theories of change.     

Matrix training of preliteracy skills with preschoolers with autism

Matrix training is a generative approach to instruction in which words are arranged in a matrix so that some multiword phrases are taught and others emerge without direct teaching. We taught 4 preschoolers with autism to follow instructions to perform action-picture combinations (e.g., circle the pepper, underline the deer). Each matrix contained 6 actions on 1 axis and 6 pictures on the other axis. We used most-to-least prompting to train the instructions along the diagonal of each matrix and probed the untrained combinations. For 2 participants, untrained responding emerged after the minimum amount of training. The other 2 participants required further training before untrained combinations emerged. At the end of the study, 3 of the 4 participants performed the trained actions with previously known pictures, letters, and numbers. This study demonstrated that matrix training is an efficient approach to teaching language and literacy skills to children with autism.     

Using behavioral skills training to promote safe and correct staff guarding and ambulation distance of students with multiple physical disabilities

The study analyzed the effects of self-recording and behavioral skills training on guarding responses of 3 staff members while they assisted 3 students with multiple disabilities to ambulate. The intervention increased the percentage of correct posture and guarding responses and the distance that students ambulated. These effects generalized when staff taught new students.     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.