Characterization and Prediction of Suicide Plans
Linthicum, Kathryn P. (author)
Ribeiro, Jessica D. (Jessica Diana) (professor directing thesis)
Joiner, Thomas (committee member)
Schatschneider, Christopher (committee member)
Florida State University (degree granting institution)
College of Arts and Sciences (degree granting college)
Department of Psychology (degree granting department)
Each year millions of people develop suicide plans. These plans are consistently cited as critical indicators of suicide risk by clinicians, researchers, and suicide prevention agencies. Having the ability to accurately predict suicide plans, even without a full understanding of the causal risk mechanisms underlying them, would allow us to better identify individuals who require intervention – a critical step toward suicide prevention. The present study sought to describe the nature of suicide plans in the short term and predict suicide plans and related outcomes. Secondary data analysis was performed on an existing longitudinal dataset composed of 1021 participants. Participants were asked to complete suicide-related measures at baseline and 3-, 14-, and 28-days post-baseline. Analyses focused on three key outcomes: (a) suicide plans, (b) suicide preparatory behaviors, and (c) suicidal intent. To describe the nature of suicide plans in the short term, cross-sectional associations were calculated between these outcomes and relevant psychological constructs. To examine our ability to predict these outcomes, univariate logistic regression, multivariate logistic regression, and machine learning models were implemented. Results indicated that nearly all participants in the study sample endorsed a lifetime history of suicide plans. While most participants endorsed thinking of their suicide plan over the 28-day follow-up period, intent to act on these plans was only moderate on average. All suicide plan features (i.e., method, time, place) generally demonstrated consistent, yet small, cross-sectional associations with related constructs. Univariate predictors performed at approximately chance level prediction accuracy, and in general, predictive model performance was poor for traditional multivariate and LASSO models and excellent for Random Forest models. Overall, predictive results supported a complex conceptualization of risk for suicide plans, preparation to act on plans, and increased suicidal intent. The study limitations and implications for research and clinical practice are discussed. Online supplementary materials accessible through ProQuest are a full model correlation matrix and LASSO feature selection table.
1 online resource (66 pages)
2020_Spring_Linthicum_fsu_0071N_15895_P
monographic
Florida State University
Tallahassee, Florida
A Thesis submitted to the Department of Psychology in partial fulfillment of the requirements for the degree of Master of Science.
April 8, 2020.
complexity, machine learning, suicide, suicide plans, suicide risk
Includes bibliographical references.
Jessica D. Ribeiro, Professor Directing Thesis; Thomas E. Joiner, Committee Member; Chris W. Schatschneider, Committee Member.
complexity, machine learning, suicide, suicide plans, suicide risk
April 8, 2020.
A Thesis submitted to the Department of Psychology in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Jessica D. Ribeiro, Professor Directing Thesis; Thomas E. Joiner, Committee Member; Chris W. Schatschneider, Committee Member.
Characterization and Prediction of Suicide Plans
Linthicum, Kathryn P. (author)
Ribeiro, Jessica D. (Jessica Diana) (professor directing thesis)
Joiner, Thomas (committee member)
Schatschneider, Christopher (committee member)
Florida State University (degree granting institution)
College of Arts and Sciences (degree granting college)
Department of Psychology (degree granting department)
text
master thesis
Each year millions of people develop suicide plans. These plans are consistently cited as critical indicators of suicide risk by clinicians, researchers, and suicide prevention agencies. Having the ability to accurately predict suicide plans, even without a full understanding of the causal risk mechanisms underlying them, would allow us to better identify individuals who require intervention – a critical step toward suicide prevention. The present study sought to describe the nature of suicide plans in the short term and predict suicide plans and related outcomes. Secondary data analysis was performed on an existing longitudinal dataset composed of 1021 participants. Participants were asked to complete suicide-related measures at baseline and 3-, 14-, and 28-days post-baseline. Analyses focused on three key outcomes: (a) suicide plans, (b) suicide preparatory behaviors, and (c) suicidal intent. To describe the nature of suicide plans in the short term, cross-sectional associations were calculated between these outcomes and relevant psychological constructs. To examine our ability to predict these outcomes, univariate logistic regression, multivariate logistic regression, and machine learning models were implemented. Results indicated that nearly all participants in the study sample endorsed a lifetime history of suicide plans. While most participants endorsed thinking of their suicide plan over the 28-day follow-up period, intent to act on these plans was only moderate on average. All suicide plan features (i.e., method, time, place) generally demonstrated consistent, yet small, cross-sectional associations with related constructs. Univariate predictors performed at approximately chance level prediction accuracy, and in general, predictive model performance was poor for traditional multivariate and LASSO models and excellent for Random Forest models. Overall, predictive results supported a complex conceptualization of risk for suicide plans, preparation to act on plans, and increased suicidal intent. The study limitations and implications for research and clinical practice are discussed. Online supplementary materials accessible through ProQuest are a full model correlation matrix and LASSO feature selection table.
complexity, machine learning, suicide, suicide plans, suicide risk
April 8, 2020.
A Thesis submitted to the Department of Psychology in partial fulfillment of the requirements for the degree of Master of Science.
Includes bibliographical references.
Jessica D. Ribeiro, Professor Directing Thesis; Thomas E. Joiner, Committee Member; Chris W. Schatschneider, Committee Member.
Florida State University
2020_Spring_Linthicum_fsu_0071N_15895