16 research outputs found
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Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks
Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8–2323), posts (29–28,684), and Gini coefficients (0.15–0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527–0.835, p < .001), and Gini coefficients and number of posts (0.342–0.725, p < .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276, p < .036). Linear regression models had positive but mixed R2 results (0.333–0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant (t = 3.346–7.381, p < .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks (t = −4.305 and −5.934, p < .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required
Integrating Positive and Clinical Psychology: Viewing Human Functioning as Continua from Positive to Negative Can Benefit Clinical Assessment, Interventions and Understandings of Resilience
In this review we argue in favour of further integration between the disciplines of positive and clinical psychology. We argue that most of the constructs studied by both positive and clinical psychology exist on continua ranging from positive to negative (e.g., gratitude to ingratitude, anxiety to calmness) and so it is meaningless to speak of one or other field studying the “positive” or the “negative”. However, we highlight historical and cultural factors which have led positive and clinical psychologies to focus on different constructs; thus the difference between the fields is more due to the constructs of study rather than their being inherently “positive” or “negative”. We argue that there is much benefit to clinical psychology of considering positive psychology constructs because; (a) constructs studied by positive psychology researchers can independently predict wellbeing when accounting for traditional clinical factors, both cross-sectionally and prospectively, (2) the constructs studied by positive psychologists can interact with risk factors to predict outcomes, thereby conferring resilience, (3) interventions that aim to increase movement towards the positive pole of well-being can be used encourage movement away from the negative pole, either in isolation or alongside traditional clinical interventions, and (4) research from positive psychology can support clinical psychology as it seeks to adapt therapies developed in Western nations to other cultures