Risk Factors for Suicidal Thoughts and Behaviors: A Meta-Analysis of 50 Years of Research

By Joseph C. Franklin, Jessica D. Ribeiro, K. Fox, Kate H Bentley, Evan M. Kleiman, Xieyining Huang, Katherine M. Musacchio, A. Jaroszewski, B. Chang, Matthew K. Nock. Psychological bulletin, 2017

3,224 citations recorded by the source corpus.

Subfields: Clinical Psychology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/fee4db01f6f981931dfd87376a8f861353d1e494

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Citations in the corpus

Think Locally, Act Globally: Federated Learning with Local and Global Representations (2020, background): “Despite these alarming statistics, there is little consensus concerning imminent risk for suicide [18, 26].”
Natural Language Processing of Social Media as Screening for Suicide Risk (2018, result): “A direct point of comparison for performance from clinicians is difficult to match exactly, but the results from Franklin et al 4 suggest that the equivalent expected proportion of users identified as “at risk” by clinicians who would go on to attempt suicide would be in the 4% to 6%…”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, methodology): “…perform a linear transformation to capture macroscopic neighborhood structures on the Poincaré ball manifold, followed by a Frechet Mean operation (Fréchet, 1948) denoted by FM. Owing to the trainable curvatureK, Hyper-SOS utilizes a hyperbolic non-linear activation with varying curvature…”
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task (2021, background): “As important motivating background, a meta-analysis by Franklin et al. (2017) concludes that prediction of suicidal thoughts and behaviors has not improved in ﬁfty years, encouraging a shift to algorithmic and machine learning approaches.”
Examination of Real-Time Fluctuations in Suicidal Ideation and Its Risk Factors: Results From Two Ecological Momentary Assessment Studies (2017, background): “As noted in a recent meta-analysis on the prediction of suicidal outcomes (Franklin et al., 2017), less than 1% of suicide risk factor research is focused on time frames of a week or less, the period most important to clinicians tasked with making decisions about the short-term risk of their…”
The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, background): “…that most people who attempt suicide have a mental disorder, but because the converse proposition (i.e., “most people with mental disorders attempt sui-cide”) is not true, the relative contribution of this risk factor to suicide prediction is low (Franklin et al., 2017; Ribeiro et al., 2019).”