Does interpreter-mediated CBT with traumatized refugee people work? A comparison of patient outcomes in East London

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Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs

In this work we show that, using the eigen-decomposition of the adjacency matrix, we can consistently estimate latent positions for random dot product graphs provided the latent positions are i.i.d. from some distribution. If class labels are observed for a number of vertices tending to infinity, then we show that the remaining vertices can be classified with error converging to Bayes optimal using the $k$-nearest-neighbors classification rule. We evaluate the proposed methods on simulated data and a graph derived from Wikipedia

Matched Filters for Noisy Induced Subgraph Detection

The problem of finding the vertex correspondence between two noisy graphs with different number of vertices where the smaller graph is still large has many applications in social networks, neuroscience, and computer vision. We propose a solution to this problem via a graph matching matched filter: centering and padding the smaller adjacency matrix and applying graph matching methods to align it to the larger network. The centering and padding schemes can be incorporated into any algorithm that matches using adjacency matrices. Under a statistical model for correlated pairs of graphs, which yields a noisy copy of the small graph within the larger graph, the resulting optimization problem can be guaranteed to recover the true vertex correspondence between the networks. However, there are currently no efficient algorithms for solving this problem. To illustrate the possibilities and challenges of such problems, we use an algorithm that can exploit a partially known correspondence and show via varied simulations and applications to {\it Drosophila} and human connectomes that this approach can achieve good performance.Comment: 41 pages, 7 figure

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements. We study this problem from a statistical framework in which one of the graphs is an errorfully observed copy of the other. We introduce a corrupting channel model, and show that in this model framework, the solution to the graph matching problem is a maximum likelihood estimator. Necessary and sufficient conditions for consistency of this MLE are presented, as well as a relaxed notion of consistency in which a negligible fraction of the vertices need not be matched correctly. The results are used to study matchability in several families of random graphs, including edge independent models, random regular graphs and small-world networks. We also use these results to introduce measures of matching feasibility, and experimentally validate the results on simulated and real-world networks

A new scale to assess the therapeutic relationship in community mental health care: STAR

Background. No instrument has been developed specifically for assessing the clinician-patient therapeutic relationship (TR) in community psychiatry. This study aimed to develop a measure of the TR with clinician and patient versions using psychometric principles for test construction. Method. A four-stage prospective study was undertaken, comprising qualitative semi-structured interviews about TRs with clinicians and patients and their assessment of nine established scales for their applicability to community care, administering an amalgamated scale of more than 100 items, followed by Principal Components Analysis (PCA) of these ratings for preliminary scale construction. test-retest reliability of the scale and administering the scale in a new sample to confirm its factorial structure. The sample consisted of patients with severe mental illness and a designated key worker in the care of 17 community mental health teams in England and Sweden. Results. New items not covered by established scales were identified, including clinician helpfulness in accessing services, patient aggression and family interference. The new patient (STAR-P) and clinician scales (STAR-C) each have 12 items comprising three subscales: positive collaboration and positive clinician input in both versions, non-supportive clinician input in the patient version, and emotional difficulties in the clinician version. Test-retest reliability was r = 0(.)76 for STAR-P and r = 0(.)68 for STAR-C. The factorial structure of the new scale was confirmed with a good fit. Conclusions. STAR is a specifically developed, brief scale to assess TRs in community psychiatry with good psychometric properties and is suitable for use in research and routine care

Friends and Symptom Dimensions in Patients with Psychosis: A Pooled Analysis

PMCID: PMC3503760This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited