A framework for exploring the sense of community and social life in residential environments

Sense of community and social life are two key concepts related to social cohesion, which have been the subject of extensive studies in several disciplines including sociology, psychology and built environment. Social life studies have been mostly conducted in the built environment discipline focusing on city centres; while sense of community studies were mostly the target of sociologists and psychologists focusing on neighbourhoods. As a result, the role of the built environment on the sense of community and social life of neighbourhoods is considered as a missing gap in the literature. This paper, through defining the concepts of social life and sense of community, aims to develop a conceptual framework for further implementation in future research. Accurate implication and interpretation of the concepts show that neighbourhoods can include the sense of community in the residential environment and the social life in the commercial environment. This is because residential environments are where residents' requirements can be met through their commitment to the community and commercial environments are the fulcrum of interaction and communication.

Power, effects, confidence, and significance: an investigation of statistical practices in nursing research

Objectives: To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported. Design: Statistical review. Data sources: Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors. Review methods: Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified. Results: The median power to detect small, medium, and large effect sizes was .40 (interquartile range [. IQR]. = .24-.71), .98 (IQR= .85-1.00), and 1.00 (IQR= 1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR= .26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers. Conclusion: The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies. © 2013 .

Online mindfulness-based intervention for late-stage bipolar disorder: pilot evidence for feasibility and effectiveness

OBJECTIVES: People in the late stage of bipolar disorder (BD) experience elevated relapse rates and poorer quality of life (QoL) compared with those in the early stages. Existing psychological interventions also appear less effective in this group. To address this need, we developed a new online mindfulness-based intervention targeting quality of life (QoL) in late stage BD. Here, we report on an open pilot trial of ORBIT (online, recovery-focused, bipolar individual therapy). METHODS: Inclusion criteria were: self-reported primary diagnosis of BD, six or more episodes of BD, under the care of a medical practitioner, access to the internet, proficient in English, 18-65 years of age. Primary outcome was change (baseline - post-treatment) on the Brief QoL.BD (Michalak and Murray, 2010). Secondary outcomes were depression, anxiety, and stress measured on the DASS scales (Lovibond and Lovibond, 1993). RESULTS: Twenty-six people consented to participate (Age M=46.6 years, SD=12.9, and 75% female). Ten participants were lost to follow-up (38.5% attrition). Statistically significant improvement in QoL was found for the completers, t(15)=2.88, 95% CI:.89-5.98, p=.011, (Cohen׳s dz=.72, partial η(2)=.36), and the intent-to-treat sample t(25)=2.65, 95% CI:.47-3.76, (Cohen׳s dz=.52; partial η(2)=.22). A non-significant trend towards improvement was found on the DASS anxiety scale (p=.06) in both completer and intent-to-treat samples, but change on depression and stress did not approach significance. LIMITATIONS: This was an open trial with no comparison group, so measured improvements may not be due to specific elements of the intervention. Structured diagnostic assessments were not conducted, and interpretation of effectiveness was limited by substantial attrition. CONCLUSION: Online delivery of mindfulness-based psychological therapy for late stage BD appears feasible and effective, and ORBIT warrants full development. Modifications suggested by the pilot study include increasing the 3 weeks duration of the intervention, adding cautions about the impact of extended meditations, and addition of coaching support/monitoring to optimise engagement.

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique. © 2008 IEEE.