This review of meta-analyses of outcome studies of adults receiving Computer-Based Health Education (CBHE) has two goals. The first is to provide an overview of the efficacy of CBHE interventions, and the second is to identify moderators of these effects. A systematic literature search resulted in 15 meta-analyses of 278 controlled outcome studies. The meta-analyses were analysed with regard to reported (overall) effect sizes, heterogeneity and interaction effects. The results indicate a positive relationship between CBHE interventions and improvements in health-related outcomes, with small overall effect sizes compared to non-computer-based interventions. The sustainability of the effects was observed for up to six months. Outcome moderators (31 variables) were studied in 12 meta-analyses and were clustered into three categories: intervention features (20 variables), participant characteristics (five variables) and study features (six variables). No relationship with effectiveness was found for four intervention features, theoretical background, use of internet and e-mail, intervention setting and self-monitoring; two participant features, age and gender; and one study feature, the type of analysis. Regarding the other 24 identified features, no consistent results were observed across meta-analyses. To enhance the effectiveness of CBHE interventions, moderators of effects should be studied as single constructs in high-quality study designs. http://www.journalofinterdisciplinarysciences.com/ https://www.linkedin.com/in/leontienvreeburg/
Computer security incident response teams (CSIRTs) respond to a computer security incident when the need arises. Failure of these teams can have far-reaching effects for the economy and national security. CSIRTs often have to work on an ad hoc basis, in close cooperation with other teams, and in time constrained environments. It could be argued that under these working conditions CSIRTs would be likely to encounter problems. A needs assessment was done to see to which extent this argument holds true. We constructed an incident response needs model to assist in identifying areas that require improvement. We envisioned a model consisting of four assessment categories: Organization, Team, Individual and Instrumental. Central to this is the idea that both problems and needs can have an organizational, team, individual, or technical origin or a combination of these levels. To gather data we conducted a literature review. This resulted in a comprehensive list of challenges and needs that could hinder or improve, respectively, the performance of CSIRTs. Then, semi-structured in depth interviews were held with team coordinators and team members of five public and private sector Dutch CSIRTs to ground these findings in practice and to identify gaps between current and desired incident handling practices. This paper presents the findings of our needs assessment and ends with a discussion of potential solutions to problems with performance in incident response. https://doi.org/10.3389/fpsyg.2017.02179 LinkedIn: https://www.linkedin.com/in/rickvanderkleij1/
MULTIFILE
Abstract Despite the numerous business benefits of data science, the number of data science models in production is limited. Data science model deployment presents many challenges and many organisations have little model deployment knowledge. This research studied five model deployments in a Dutch government organisation. The study revealed that as a result of model deployment a data science subprocess is added into the target business process, the model itself can be adapted, model maintenance is incorporated in the model development process and a feedback loop is established between the target business process and the model development process. These model deployment effects and the related deployment challenges are different in strategic and operational target business processes. Based on these findings, guidelines are formulated which can form a basis for future principles how to successfully deploy data science models. Organisations can use these guidelines as suggestions to solve their own model deployment challenges.
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