Evidence map›Paper›PMID 36612788›Full record

ArticleInternational journal of environmental research and public health2022

Suicide Possibility Scale Detection via Sina Weibo Analytics: Preliminary Results.

Yun Gu, Deyuan Chen, Xiaoqian Liu

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yun GuSchool of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.
Deyuan ChenSchool of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.
Xiaoqian LiuInstitute of Psychology, Chinese Academy of Sciences, Beijing 100101, China.ORCID 0000-0003-2740-8420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Suicide, as an increasingly prominent social problem, has attracted widespread social attention in the mental health field. Traditional suicide clinical assessment and risk questionnaires lack timeliness and proactivity, and high-risk groups often conceal their intentions, which is not conducive to early suicide prevention. In this study, we used machine-learning algorithms to extract text features from Sina Weibo data and built a suicide risk-prediction model to predict four dimensions of the Suicide Possibility Scale-hopelessness, suicidal ideation, negative self-evaluation, and hostility-all with model validity of 0.34 or higher. Through this method, we can detect the symptoms of suicidal ideation in a more detailed way and improve the proactiveness and accuracy of suicide risk prevention and control.

Indexed as

SuicideSuicide, AttemptedHumansSuicidal IdeationSuicide PreventionSurveys and Questionnairesmachine learningsuicidal ideationsuicide possibility scale

Identifiers

PMID36612788
PMCPMC9819932

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.