The stigma associated with mental disorders is a global public health problem. Programs to combat it must be informed by the best available evidence. To this end, a meta-analysis was undertaken to investigate the effectiveness of existing programs. A systematic search of PubMed, PsycINFO and Cochrane databases yielded 34 relevant papers, comprising 33 randomized controlled trials. Twenty-seven papers (26 trials) contained data that could be incorporated into a quantitative analysis. Of these trials, 19 targeted personal stigma or social distance (6,318 participants), six addressed perceived stigma (3,042 participants) and three self-stigma (238 participants). Interventions targeting personal stigma or social distance yielded small but significant reductions in stigma across all mental disorders combined (d50.28, 95% CI: 0.17-0.39, p<0.001) as well as for depression (d50.36, 95% CI: 0.10-0.60, p<0.01), psychosis (d50.20, 95% CI: 0.06-0.34, p<0.01) and generic mental illness (d50.30, 95% CI: 0.10-0.50, p<0.01). Educational interventions were effective in reducing personal stigma (d50.33, 95% CI: 0.19-0.42, p<0.001) as were interventions incorporating consumer contact (d50.47, 95% CI: 0.17-0.78, p<0.001), although there were insufficient studies to demonstrate an effect for consumer contact alone. Internet programs were at least as effective in reducing personal stigma as face-to-face delivery. There was no evidence that stigma interventions were effective in reducing perceived or self-stigma. In conclusion, there is an evidence base to inform the roll out of programs for improving personal stigma among members of the community. However, there is a need to investigate methods for improving the effectiveness of these programs and to develop interventions that are effective in reducing perceived and internalized stigma.
Sustainable online peer-to-peer support groups require engaged members. A metric commonly used to identify these members is the number of posts they have made. The 90-9-1 principle has been proposed as a 'rule of thumb' for classifying members using this metric with a recent study demonstrating the applicability of the principal to digital health social networks. Using data from a depression Internet support group, the current study sought to replicate this finding and to investigate in more detail the model of best fit for classifying participant contributions. Our findings replicate previous results and also find the fit of a power curve (Zipf distribution) to account for 98.6% of the variance. The Zipf distribution provides a more nuanced image of the data and may have practical application in assessing the 'coherence' of the sample.
BackgroundThe use of amphetamine-type stimulants (ATS) places a large burden on health services.ObjectiveThe aim was to evaluate the effectiveness of a self-guided Web-based intervention (“breakingtheice”) for ATS users over 6 months via a free-to-access site.MethodsWe conducted a randomized trial comparing a waitlist control with a fully automated intervention containing 3 modules derived from cognitive behavioral therapy and motivation enhancement. The main outcome was self-reported ATS use in the past 3 months assessed at 3- and 6-month follow-ups using the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). Secondary outcomes were help-seeking intentions (general help-seeking questionnaire), actual help seeking (actual help-seeking questionnaire), psychological distress (Kessler 10), polydrug use (ASSIST), quality of life (European Health Interview Survey), days out of role, and readiness to change. Follow-up data were evaluated using an intention-to-treat (ITT) analysis with a group by time interaction.ResultsWe randomized 160 people (intervention: n=81; control: n=79). At 6 months, 38 of 81 (47%) intervention and 41 of 79 (52%) control participants provided data. ATS scores significantly declined for both groups, but the interaction effect was not significant. There were significant ITT time by group interactions for actual help seeking (rate ratio [RR] 2.16; d=0.45) and help-seeking intentions (RR 1.17; d=0.32), with help seeking increasing for the intervention group and declining for the control group. There were also significant interactions for days completely (RR 0.50) and partially (RR 0.74) out of role favoring the intervention group. However, 37% (30/81) of the intervention group did not complete even 1 module.ConclusionsThis self-guided Web-based intervention encouraged help seeking associated with ATS use and reduced days out of role, but it did not reduce ATS use. Thus, this program provides a means of engaging with some sections of a difficult-to-reach group to encourage treatment, but a substantial minority remained disengaged.Trial RegistrationAustralian and New Zealand Clinical Trials Registry: ACTRN12611000947909; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=343307 (Archived by WebCite at http://www.webcitation.org/6Y0PGGp8q).
Over the last several years, there has been a substantial increase in the number of publications reporting on Internet interventions for mental health and addictions. This paper provides a summary of the recent research on Internet interventions for the most common mental health and addictions concerns-depression, anxiety, alcohol and smoking. There is considerable evidence for the effectiveness of Internet-based interventions targeting depression, anxiety disorders, alcohol use and smoking. Small to moderate effect sizes have been reported for interventions targeting depression, anxiety and alcohol use, and smoking interventions have shown large effects. The addition of human support to depression and anxiety interventions has generally resulted in larger treatments effects, but this trend has not been observed in trials of interventions targeting alcohol use. There is some evidence that online interventions can be as effective as face-to-face therapies, at least for anxiety disorders. Despite a proliferation of research activity in this area, gaps in knowledge remain. Future research should focus on the development and evaluation of interventions for different platforms (e.g. smartphone applications), examining the long-term impacts of these interventions, determining active intervention components and identifying methods for enhancing tailoring and engagement. Careful consideration should be given to the ongoing technical and clinical expertise required to ensure that Internet interventions are delivered safely and professionally in a rapidly changing technology environment.
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