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• Smith AR, Zuromski K, Dodd DR. Eating disorders and suicidality: What we know, what we don’t know, and suggestions for future research. Curr Opin Psychol. 2018;22:63–7. https://doi.org/10.1016/j.copsyc.2017.08.023. This paper reviews the current suicidality literature within each eating disorder diagnosis and discussed the theoretical explanations for the elevated risk for suicidality among those with eating disorders.
• Bryan CJ, Rudd DM. The importance of temporal dynamics in the transition from suicidal thought to behavior. Clin Psychol Sci Prac. 2016;23:21–5. https://doi.org/10.1111/cpsp.12135. This work discussed how the fluid vulnerability of theory of suicide provides a working model for conceptualizing suicide risk.
• Nock MK, Kesser RC, Franklin JC. Risk factors for suicide ideation differ from those for the transition to suicide attempt: The importance of creativity, rigor, and urgency in suicide research. Clin Psychol Sci Prac. 2016;23:31–4. https://doi.org/10.1111/cpsp.12133. The authors of this work commented on the article by May and Klonsky (2016). The authors focus their commentary on how the meta-analysis omitted important studies and discussed how the meta-analysis was designed in a way that precludes the examination of actual risk factors for the transition from suicidal ideation to attempt.
• May MM, Klonsky DE. What distinguishes suicide attempters from suicide ideators? A meta-analysis of potential factors. Clin Psychol Sci Prac. 2016;23:5–20. https://doi.org/10.1111/cpsp.12136. The authors of the study investigated what differentiates attempters from ideators by analyzing 27 studies. Depression, alcohol use disorders, hopelessness, gender, race, marital status, and education all were similar between the two groups. The authors found that anxiety disorders, trauma, drug use disorders, and sexual abuse history were moderately elevated in attempters compared to ideators.
• Kessler RC, Warner CH, Ivany C, Petukhova MV, Rose S, Bromet EJ, et al. Predicting suicides after psychiatric hospitalization in US Army soldiers: the Army study to assess risk and resilience in service members (Army STARRS). JAMA Psychiatry. 2015;72(1):49–57. https://doi.org/10.1001/jamapsychiatry.2014.1754. This study used administrative data available before hospital discharge to predict suicides using machine learning methods. The strongest predictors were male sex, late age of military enlistment, criminal offenses, prior suicidality, and possessing weapons.
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