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.     

Development and Validation of the Sexist Attitudes Toward Women Stale (SATWS)

Although sexism is now defined as a major social problem, there have been very few empirical studies investigating its correlates and determinants. One primary reason for this is that there are no published sexism scales, and hence it is difficult to assess individual differences in sexism. This article describes the development and validation of a 40-item Sexist Attitudes Toward Women Scale (SATWS). On rational-intuitive grounds, it is hypothesized that sexism toward women has seven components and, accordingly, items were written to reflect these. It is suggested that scales measuring only one of these components (e.g., sex-role stereotype scales and women's liberation movement scales) lack content validity as measures of sexism. On empirical grounds it is argued that the SATW scale also has stronger construct validity as a measure of sexism toward women than other such scales. The SATW scale has high internal consistency reliability for both college students and nonstudent adults and the SATW scale scores are independent of social desirability scores. Relationships to age, sex, and education are presented.