Predicting self-perceived risk of suicide: A Bayesian multilevel analysis of suicide status form constructs using ambulatory assessment.

Abstract

Introduction: The Collaborative Assessment of Suicidality (CAMS) is an effective clinical framework to treat suicidal thoughts and behaviours. CAMS employs the Suicide Status Form (SSF) to monitor suicide risk factors across sessions. The first six items (termed the SSF Core Assessment) include psychological pain, stress, agitation, hopelessness, self-hate and self-reported overall behavioural risk for suicide. The first five are theoretically derived risk factors. While CAMS employs session-by-session tracking, no research has investigated how the five risk factors vary together and concurrently and prospectively predict self-perceived suicide risk when assessed via ambulatory assessment.

Methods: Undergraduates (n = 52) with past-2-week suicidal ideation completed 5 ambulatory assessment surveys of the SSF Core Assessment variables daily over 10 days. Descriptive and variability statistics were used to describe the core assessment constructs. Multilevel models were used to examine how the first five items predicted overall behavioural risk of suicide.

Results: Each of the SSF Core Assessment variables demonstrated variability over the study period, with stress exhibiting the greatest and overall risk the lowest variability. Self-hate and hopelessness concurrently predicted self-reported overall risk for suicide within the same model. Only self-hate and agitation were significantly prospectively predictive of overall behavioural risk, even after controlling for suicidal ideation.

Conclusions: This is the first study to provide insight into the short-term variability and predictive capabilities of the SSF Core Assessment constructs. Each construct varies considerably over short time scales, suggesting that more frequent monitoring of these constructs may be an important consideration within future CAMS treatment research.