Recovery, empowerment and rehabilitation: Do inpatient psychiatric rehabilitation services empower the individual?

Perceptions of the course and outcome from serious mental illness have changed over the last century and, more recently, the concept of recovery has gained prominence in this field. This paper reviews recent literature on recovery from serious mental illness and discusses both the meaning of the concept and the key contributing factors. Research suggests that empowerment is one of the most salient factors contributing to recovery and the relationship between recovery and empowerment is examined. Most research in the area of empowerment has, to date, focused on community settings and this paper considers the relevance of these ideas in other mental health settings. The relationship between empowerment, recovery and mental health services is discussed. Finally, conclusions are drawn and recommendations for further research are outlined

Empirical convergence for C. thermocellum iSR432.

<p>(a) Convergence according to the Geweke diagnostic. (b) Convergence according to the Heidelberger-Welch diagnostic. Convergence for <i>optGpSampler</i> is observed at approximately 500 steps. For <i>gpSampler</i>, both diagnostics only agree on convergence after  = 5000 steps. Notice the higher HW convergence fraction of the latter at  = 50 steps and at  = 5000 steps compared to the steps in between.</p

Empirical convergence for E. coli iAF1260.

<p>(a) Convergence according to the Geweke diagnostic. (b) Convergence according to the Heidelberger-Welch diagnostic. For both convergence tests, <i>gpSampler</i>’s performance deteriorates when the step count is increased up to  = 2500. Especially the good scores at low values of seem unrealistic and could indicate convergence towards a non-uniform distribution. Results for <i>optGpSampler</i> seem more stable and more reliable.</p

Algorithm 1.

<p>optGpSampler(, , , ).</p

Illustration of hit-and-run.

<p>Hit-and-run starts at the point in the solution space . It chooses a random direction and determines the maximum distance it can travel forwards or backwards in that direction. A random step size is chosen on the line . The next point is obtained by travelling in the direction . By iterating this process times, samples are obtained that are uniformly distributed in the space, when .</p

-deviation for C. thermocellum iSR432.

<p>-deviation from samples obtained with sampler at step count to sampler using  = 5000. (a) Deviation to samples obtained by  = <i>gpSampler</i>. (b) Deviation from samples obtained by  = <i>optGpSampler</i>. In both cases <i>optGpSampler</i> converges much faster to sampler .</p

-deviation for E. coli iAF1260 -deviation from samples obtained with sampler at step count to sampler using = 5000.

<p>(a) Deviation to samples obtained by  = <i>gpSampler</i>. (b) Deviation from samples obtained by  = <i>optGpSampler</i>. Self-deviation () is small for both samplers, but there is a large cross-deviation (). For this large network, the we do not observe convergence of <i>gpSampler</i> to the samples obtained by <i>optGpSampler</i> or vice versa as in Fig. 4.</p

Runtimes for the networks analysed.

<p>The number of metabolites and reactions is denoted by <i>m</i> and <i>n</i> respectively. The dimensionality of the nullspace of , is given by <i>N(S)</i>. <i>Time gp (SD)</i> is the mean runtime (seconds) and standard deviation for sampling  = 50.000 points using <i>gpSampler</i>. <i>Time optGp (SD)</i> denotes the same figures for <i>optGpSampler</i>.</p><p>Experiments were performed on a 16 GB RAM AMD Phenom desktop pc.</p

Conceptual difference between the samplers.

<p>Conceptual difference between (a) ACHR, (b) gpSampler and (c) optGpSampler. Warm-up points are depicted as gray rectangles, samples that are stored as gray circles. Uncoloured circles denote points that are visited by the sampler, but are not stored as a sample. a) The original ACHR algorithm starts at a point and iteratively moves to a next point  = . One chain is used, with step count  = 1. The chain contains warm-up points and samples. b) <i>GpSampler</i> uses the linear programming procedure described in the main text to find warm-up points. Then, each of the warm-up points is iteratively moved in the space in the same fashion as the ACHR algorithm in (a), leading to sampling chains. Each chain of length returns its end point as a sample. c) <i>OptGpSampler</i> obtains warm-up points. For each of the processors used, a warm-up point is chosen randomly as the initial point , with . Starting from the warm-up points, new points are found in the same fashion as for the ACHR algorithm in (a), but now only every point is kept as a sample. Again, the result is sample points, but now these have travelled up to steps from a warm-up point. Compared to <i>gpSampler</i>, it uses less but much longer sampling chains.</p

Additional file 5: of CTCF-mediated chromatin loops enclose inducible gene regulatory domains

A custom Python script. (ZIP 7kb