Perceived Racism and Suicide Ideation: Mediating Role of Depression but Moderating Role of Religiosity among African American Adults

Suicide is a public health problem for African Americans who are young and of working age. The purpose of this study was to examine mediated and moderated effects of perceived racism on suicide ideation in a community sample of 236 African American men and women. Measures of suicide ideation, depression symptoms, intrinsic/extrinsic religiosity, and perceived racism were administered. Perceived racial discrimination was directly and indirectly associated with suicide ideation. For participants who reported low levels of extrinsic religiosity, the mediated effect of perceived racism (via depression symptoms) was significant. These findings provide some insight into suicide vulnerability for specific subgroups of African Americans.     

International and Domestic Students,Perceived Burdensomeness,Belongingness, and Suicidal Ideation

Suicide is the second leading cause of death for traditional‐age college students, and the interpersonal theory of suicide (ITS; Joiner, 2005) provides a cogent framework for predicting which students may be at highest risk. However, little is known about how constructs of ITS operate in cross‐cultural contexts. Findings, based on a sample of international and domestic undergraduate students (N = 254), indicated that the ITS construct of perceived burdensomeness was positively associated with suicidal ideation (SI) for both groups. However, campus belongingness emerged as connected with SI for international students, whereas family belongingness emerged as connected with SI for domestic students.     

How does orthographic knowledge influence performance on phonological awareness tasks?

Three experiments explored the nature of orthographic influences on performance on phonological awareness tasks. Experiment 1 demonstrated that adults find it easier to perform phoneme deletions on items where there is a direct correspondence between letters and target sounds (e.g., take the /r@/ from struggle) than where there is not (e.g., take the /w@/ from squabble). Analogous results were found in a phoneme reversal task. Spelling production ability tended to correlate more strongly with performance on the former type of item than on the latter, suggesting that elevated performance on phonological awareness tasks is associated with the use of orthographic information. Experiment 2 produced similar results in Grade 5 children. Experiment 3 suggested that adults cannot inhibit orthographic activation when it is disadvantageous to them, as they performed no better on items such as squabble when they were presented in pure blocks than when they were presented in mixed blocks. It is concluded that there are substantial automatic orthographic influences on phonological awareness task performance that need to be taken into account in interpreting data concerning the relationship between phonological awareness and reading.     

A Systematic Review of Suicide Prevention Programs for Military or Veterans

Military personnel and veterans have important suicide risk factors. After a systematic review of the literature on suicide prevention, seven (five in the U.S.) studies of military personnel were identified containing interventions that may reduce the risk of suicide. The effectiveness of the individual components was not assessed, and problems in methodology or reporting of data were common. Overall, multifaceted interventions for active duty military personnel are supported by consistent evidence, although of very mixed quality, and in some cases during intervals of declines in suicide rates in the general population. There were insufficient studies of U.S. Veterans to reach conclusions.     

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.