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 法学 内科学 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:3633
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
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
玄xuan发布了新的文献求助10
2秒前
GaCf发布了新的文献求助10
2秒前
慕青应助拼搏耷采纳,获得10
3秒前
传奇3应助苗条三问采纳,获得10
4秒前
5秒前
立青完成签到,获得积分10
6秒前
9秒前
麻辣小龙虾完成签到,获得积分10
10秒前
Hello应助SnowyKwok采纳,获得10
10秒前
NexusExplorer应助立青采纳,获得10
10秒前
Lily完成签到,获得积分10
11秒前
完美世界应助SRsora采纳,获得10
11秒前
Twonej应助粥粥爱糊糊采纳,获得30
12秒前
Zz发布了新的文献求助10
12秒前
默cm完成签到,获得积分10
13秒前
呆萌念云完成签到 ,获得积分10
13秒前
学不通发布了新的文献求助10
13秒前
NexusExplorer应助易安采纳,获得10
15秒前
dew应助可可豆战士采纳,获得100
16秒前
科研通AI6.1应助Sunnig盈采纳,获得10
19秒前
李健应助天涯明月采纳,获得10
19秒前
SSY完成签到,获得积分10
20秒前
木子李完成签到,获得积分10
21秒前
21秒前
gxs完成签到,获得积分10
21秒前
22秒前
Twonej举报zrw求助涉嫌违规
24秒前
kai完成签到,获得积分10
24秒前
24秒前
CodeCraft应助玄xuan采纳,获得10
25秒前
科研通AI6.3应助真瑞卍采纳,获得10
26秒前
我是老大应助gxs采纳,获得10
27秒前
阔达的背包完成签到 ,获得积分10
27秒前
29秒前
菊爱花发布了新的文献求助10
29秒前
2339822272发布了新的文献求助10
31秒前
abtitw完成签到,获得积分10
31秒前
吭吭菜菜完成签到,获得积分10
32秒前
34秒前
朴素代芙完成签到,获得积分10
34秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Organometallic Chemistry of the Transition Metals 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
Leading Academic-Practice Partnerships in Nursing and Healthcare: A Paradigm for Change 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6437617
求助须知:如何正确求助?哪些是违规求助? 8252063
关于积分的说明 17558310
捐赠科研通 5496115
什么是DOI,文献DOI怎么找? 2898680
邀请新用户注册赠送积分活动 1875337
关于科研通互助平台的介绍 1716355