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Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform.
Methods
Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts.
Results
The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns.
Conclusions
Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.
Research has shown that animated graphics are not the educational magic bullet that many expected them to be. They are neither necessarily superior to static graphics nor intrinsically effective in their own right. The Animation Composition Principle characterizes learning from animation as a hierarchical relation-building process by which mental models of the depicted subject matter are progressively and cumulatively constructed from discrete information primitives. It helps explain the limited success of previous attempts to improve animation’s effectiveness that took no account of their fundamental design. By giving due consideration to both perceptual and cognitive aspects of animation processing, the Animation Processing Model that embodies this Principle opens the door to novel, more effective compositional design options. These compositional animations significantly improve learning outcomes.
Trust law has grown and developed over recent years through the continued ingenuity of practitioners and the provision of innovative new trust laws by offshore jurisdictions. The wealth managed through the medium of trust law has also changed in recent years, as increasingly it has come from the newly rich of Asia. This brings distinctive issues to the fore: the role of settlors, family members and trusted advisors in trust administration; the position of trustees in relation to instructions coming from such persons; and an increased desire for confidentiality in trust administration and the settlement of trust disputes. This collection focuses on trusts which are deliberately created to manage wealth and the concomitant issues such trusts raise in other areas of law. Essays from leading members of the judiciary, practitioners and academics explore these developments and their implications for the users of trust law and for society in general.
Multimedia learning environments present combinations of text, illustrations, narration, and animation and are typically computer-based. This chapter provides a brief review of the self-explanation principle, and introduces a framework for categorizing the number of ways in which self-explanation has been operationalized. While open-ended and menu-based approaches mark the two extremes, there are a number of ways of prompting students to self-explain that fall in the middle: focused, scaffolded, and resource-based prompts. Examples of each within the context of multimedia learning are presented. It presents a number of studies whose results support the hypothesis that self-explanation prompts that provide more focus or direction are particularly beneficial for multimedia learning environments, because they foster integration across multiple sources of information and help students to develop a single, coherent representation. The chapter also discusses implications for cognitive theory and instructional design and ideas for future work.