A controlled trial of internet-based cognitive-behavioural therapy for panic disorder with face-to-face support from a general practitioner or email support from a psychologist

Background: Panic disorder (PD) is one of the most common anxiety disorders seen in general practice, but provision of evidence-based cognitive-behavioural treatment (CBT) is rare. Many Australian GPs are now trained to deliver focused psychological strategies, but in practice this is time consuming and costly.

Objective: To evaluate the efficacy of an internet-based CBT intervention (Panic Online) for the treatment of PD supported by general practitioner (GP)-delivered therapeutic assistance.

Design: Panic Online supported by GP-delivered face-to-face therapy was compared to Panic Online supported by psychologist-delivered email therapy.

Methods: Sixty-five people with a primary diagnosis of PD (78% of whom also had agoraphobia) completed 12 weeks of therapy using Panic Online and therapeutic assistance with his/her GP (n = 34) or a clinical psychologist (n = 31). The mean duration of PD for participants allocated to these groups was 59 months and 58 months, respectively. Participants completed a clinical diagnostic interview delivered by a psychologist via telephone and questionnaires to assess panic-related symptoms, before and after treatment.

Results: The total attrition rate was 20%, with no group differences in attrition frequency. Both treatments led to significant improvements in panic attack frequency, depression, anxiety, stress, anxiety sensitivity and quality of life. There were no statistically significant differences in the two treatments on any of these measures, or in the frequency of participants with clinically significant PD at post assessment.

Conclusions: When provided with accessible online treatment protocols, GPs trained to deliver focused psychological strategies can achieve patient outcomes comparable to efficacious treatments delivered by clinical psychologists. The findings of this research provide a model for how GPs may be assisted to provide evidence-based mental healthcare successfully.

Is internet-based CBT for panic disorder and agoraphobia as effective as face-to-face CBT

This study compared Panic Online (PO), an internet-based CBT intervention, to best-practice face-to-face CBT for people with panic disorder with or without agoraphobia. Eighty-six people with a primary diagnosis of panic disorder were recruited from Victoria, Australia. Participants were randomly assigned to either PO (n = 46) or best practice face-to-face CBT (n = 40). Effects of the internet-based CBT program were found to be comparable to those of face-to-face CBT. Both interventions produced significant reductions in panic disorder and agoraphobia clinician severity ratings, self reported panic disorder severity and panic attack frequency, measures of depression, anxiety, stress and panic related cognitions, and displayed improvements in quality of life. Participants rated both treatment conditions as equally credible and satisfying. Participants in the face-to-face CBT treatment group cited higher enjoyment with communicating with their therapist. Consistent with this, therapists’ ratings for compliance to treatment and understanding of the CBT material was higher in the face-to-face CBT treatment group. PO required significantly less therapist time than the face-to-face CBT condition.

Videoconferenced and face-to-face psychological supervision

Behavioural measures confirmed a greater teaching focus in early videoconferenced sessions, while speech style was consistently less interactive, compared with face-to-face. Overall supervision relationships felt closer in person, but some participants preferred the protection of distance. A positive attitude facilitated adaption to the videoconferencing modality for effective supervision.

A fast kernel dimension reduction algorithm with applications to face recognition

This paper presents a novel dimensionality reduction algorithm for kernel based classification. In the feature space, the proposed algorithm maximizes the ratio of the squared between-class distance and the sum of the within-class variances of the training samples for a given reduced dimension. This algorithm has lower complexity than the recently reported kernel dimension reduction(KDR) for supervised learning. We conducted several simulations with large training datasets, which demonstrate that the proposed algorithm has similar performance or is marginally better compared with KDR whilst having the advantage of computational efficiency. Further, we applied the proposed dimension reduction algorithm to face recognition in which the number of training samples is very small. This proposed face recognition approach based on the new algorithm outperforms the eigenface approach based on the principle component analysis (PCA), when the training data is complete, that is, representative of the whole dataset.

Non-face to face consultations and communications in primary care : the role and perspective of general practice managers in Scotland

Background Practice managers play an important role in the organisation and delivery of primary care, including uptake and implementation of technologies. Little is currently known about practice managers’ attitudes to the use of information and communication technologies, such as email or text messaging, to communicate or consult with patients.

Objectives To investigate practice managers’ attitudes to non-face-to-face consultation/communication technologies in the routine delivery of primary care and their role in the introduction and normalisation of these technologies.

Methods We carried out a mixed-methods study in Scotland, UK. We invited all practice managers in Scotland to take part in a postal questionnaire survey. A maximum variation sample of 20 survey respondents participated subsequently in in-depth qualitative interviews.

Results Practice managers supported the use of new technologies for routine tasks to manage workload and maximise convenience for patients, but a range of contextual factors such as practice list size, practice deprivation area and geographical location affected whether managers would pursue the introduction of these technologies in the immediate future. The most common objections were medico-legal concerns and lack of perceived patient demand.

Conclusion Practice managers are likely to play a central role in the introduction of new consultation/communication technologies within general practice. They hold varying views on the appropriateness of these technologies, influenced by a complex mix of contextual characteristics.Managers from areas in which the ethos of the practice prioritises personalised care in service delivery are less enthusiastic about the adoption of remote consultation/ communication technologies.

Unfolding a face : from singular to manifold

Face recognition from a single image remains an important task in many practical applications and a significant research challenge. Some of the challenges are inherent to the problem, for example due to changing lighting conditions. Others, no less significant, are of a practical nature – face recognition algorithms cannot be assumed to operate on perfect data, but rather often on data that has already been subject to pre-processing errors (e.g. localization and registration errors). This paper introduces a novel method for face recognition that is both trained and queried using only a single image per subject. The key concept, motivated by abundant prior work on face appearance manifolds, is that of face part manifolds – it is shown that the appearance seen through a sliding window overlaid over an image of a face, traces a trajectory over a 2D manifold embedded in the image space. We present a theoretical argument for the use of this representation and demonstrate how it can be effectively exploited in the single image based recognition. It is shown that while inheriting the advantages of local feature methods, it also implicitly captures the geometric relationship between discriminative facial features and is naturally robust to face localization errors. Our theoretical arguments are verified in an experimental evaluation on the Yale Face Database.

A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

An information-theoretic approach to face recognition from face motion manifolds

In this work, we consider face recognition from face motion manifolds (FMMs). The use of the resistor-average distance (RAD) as a dissimilarity measure between densities confined to FMMs is motivated in the proposed information-theoretic approach to modelling face appearance. We introduce a kernel-based algorithm that makes use of the simplicity of the closed-form expression for RAD between two Gaussian densities, while allowing for modelling of complex and nonlinear, but intrinsically low-dimensional manifolds. Additionally, it is shown how geodesically local FMM structure can be modelled, naturally leading to a stochastic algorithm for generalizing to unseen modes of data variation. Recognition performance of our method is demonstrated experimentally and is shown to exceed that of state-of-the-art algorithms. Recognition rate of 98% was achieved on a database of 100 people under varying illumination

Do cyclic changes in women's face preferences target cues to long-term health?

A shift to prefer more masculine male faces when conception risk is high may be an adaptation for finding mates with good disease resistance. We investigated whether preferences for other facial cues to long-term health also increase when conception is likely. We examined preferences for the faces of men with good health histories and for facial averageness and symmetry, two putative indicators of health during development. Preferences were tested at two points in the menstrual cycle that differed maximally in conception risk. No cyclic changes in preferences were found. We consider implications for the sexual selection of variation in preferences across the menstrual cycle.

Patient deterioration education: Evaluation of face-to-face simulation and e-simulation approaches

Background: Simulation-based education is one strategy that may be used to teach nursing students to recognize and manage patient deterioration. Method: Final-year preregistration nursing students (n=97) completed three face-to-face laboratory-based team simulations with a simulated patient (actor) and 330 students individually completed a three-scenario Web-based simulation program: FIRST2ACTWeb™. Results: Both groups achieved moderate performance scores (means: face to face, 49%; e-simulation, 69%). Course evaluations were positive, skill gain showing a greater effect size in the face-to-face program than for e-simulation, and higher satisfaction and more positive appraisal. Conclusion: Face-to-face simulation and e-simulation are effective educational strategies with e-simulation offering greater feasibility. Either strategy is likely to add value to the learning experience.

Lecturing and other face-to-face teaching – too much or too little? An assessment based on student feedback and fail rates

Institutes of higher learning are tending to reduce the amount of face-to-face teaching that they offer, and particularly through the traditional pedagogical method of lecturing. There is ongoing debate about the educational value of lectures as a teaching approach, in terms of both whether they facilitate understanding of subject material and whether they augment the student educational experience. In this study, student evaluation of teaching scores plus academic outcome (percentage of students who fail) was assessed for 236 course units offered by a science faculty at an Australian university over the course of one year. These measures were related to the degree to which lectures and other face-to-face teaching were used in these units, controlling for factors such as class size, school and year level. An information-theoretic model selection approach was employed to identify the best models and predictors of student assessments and fail rates. All the top models of student feedback included a measure reflecting amount of face-to-face teaching, with the evaluation of quality of teaching being higher in units with higher proportions of lectures. However, these models explained only 12–20% of the variation in student evaluation scores, suggesting that many other factors come into play. By contrast, units with fewer lectures have lower failure rates. These results suggest that moving away from lectures and face-to-face teaching may not harm, and indeed may improve the number of students who pass the subject, but that this may be incurred at the expense of greater dissatisfaction in students' learning experience.