Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research.

心理信息 荟萃分析 心理学 优势比 毒物控制 可能性 临床心理学 风险因素 人为因素与人体工程学 风险评估 应用心理学 梅德林 医学 统计 环境卫生 计算机科学 逻辑回归 数学 计算机安全 内科学 法学 政治学
作者
Joseph C. Franklin,Jessica D. Ribeiro,Kathryn R. Fox,Kate H. Bentley,Evan M. Kleiman,Xieyining Huang,Katherine M. Musacchio,Adam C. Jaroszewski,Bernard Chang,Matthew K. Nock
出处
期刊:Psychological Bulletin [American Psychological Association]
卷期号:143 (2): 187-232 被引量:3034
标识
DOI:10.1037/bul0000084
摘要

Suicidal thoughts and behaviors (STBs) are major public health problems that have not declined appreciably in several decades. One of the first steps to improving the prevention and treatment of STBs is to establish risk factors (i.e., longitudinal predictors). To provide a summary of current knowledge about risk factors, we conducted a meta-analysis of studies that have attempted to longitudinally predict a specific STB-related outcome. This included 365 studies (3,428 total risk factor effect sizes) from the past 50 years. The present random-effects meta-analysis produced several unexpected findings: across odds ratio, hazard ratio, and diagnostic accuracy analyses, prediction was only slightly better than chance for all outcomes; no broad category or subcategory accurately predicted far above chance levels; predictive ability has not improved across 50 years of research; studies rarely examined the combined effect of multiple risk factors; risk factors have been homogenous over time, with 5 broad categories accounting for nearly 80% of all risk factor tests; and the average study was nearly 10 years long, but longer studies did not produce better prediction. The homogeneity of existing research means that the present meta-analysis could only speak to STB risk factor associations within very narrow methodological limits-limits that have not allowed for tests that approximate most STB theories. The present meta-analysis accordingly highlights several fundamental changes needed in future studies. In particular, these findings suggest the need for a shift in focus from risk factors to machine learning-based risk algorithms. (PsycINFO Database Record
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