Evidence map›Paper›PMID 31344675›Full record

ArticleJournal of medical Internet research2019

Proactive Suicide Prevention Online (PSPO): Machine Identification and Crisis Management for Chinese Social Media Users With Suicidal Thoughts and Behaviors.

Xingyun Liu, Xiaoqian Liu, Jiumo Sun, Nancy Xiaonan Yu, Bingli Sun, Qing Li, Tingshao Zhu

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
12.7field-weighted citation impact, top 1% of its field
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 81 citations in OpenAlex.

  1. Social Media Use for Health Purposes: Systematic Review.Journal of medical Internet research · 2021
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  16. Leveraging data science to enhance suicide prevention research: a literature review.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2022
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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

7 authors at 3 institutions in 2 countries.

Xingyun LiuInstitute of Psychology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0001-7118-0465
Xiaoqian LiuInstitute of Psychology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0003-2740-8420
Jiumo SunInstitute of Psychology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0003-0584-9022
Nancy Xiaonan YuDepartment of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China (Hong Kong).ORCID 0000-0002-6371-2684
Bingli SunInstitute of Psychology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0002-2854-1904
Qing LiDepartment of Computing, The Hong Kong Polytechnic University, Hong Kong, China (Hong Kong).ORCID 0000-0003-3370-471X
Tingshao ZhuInstitute of Psychology, Chinese Academy of Sciences, Beijing, China.ORCID 0000-0003-0020-3812
City University of Hong Kong · HKInstitute of Psychology, Chinese Academy of Sciences · CNHong Kong Polytechnic University · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSuicide is a great public health challenge. Two hundred million people attempt suicide in China annually. Existing suicide prevention programs require the help-seeking initiative of suicidal individuals, but many of them have a low motivation to seek the required help. We propose that a proactive and targeted suicide prevention strategy can prompt more people with suicidal thoughts and behaviors to seek help.

objectiveThe goal of the research was to test the feasibility and acceptability of Proactive Suicide Prevention Online (PSPO), a new approach based on social media that combines proactive identification of suicide-prone individuals with specialized crisis management.

methodsWe first located a microblog group online. Their comments on a suicide note were analyzed by experts to provide a training set for the machine learning models for suicide identification. The best-performing model was used to automatically identify posts that suggested suicidal thoughts and behaviors. Next, a microblog direct message containing crisis management information, including measures that covered suicide-related issues, depression, help-seeking behavior and an acceptability test, was sent to users who had been identified by the model to be at risk of suicide. For those who replied to the message, trained counselors provided tailored crisis management. The Simplified Chinese Linguistic Inquiry and Word Count was also used to analyze the users' psycholinguistic texts in 1-month time slots prior to and postconsultation.

resultsA total of 27,007 comments made in April 2017 were analyzed. Among these, 2786 (10.32%) were classified as indicative of suicidal thoughts and behaviors. The performance of the detection model was good, with high precision (.86), recall (.78), F-measure (.86), and accuracy (.88). Between July 3, 2017, and July 3, 2018, we sent out a total of 24,727 direct messages to 12,486 social media users, and 5542 (44.39%) responded. Over one-third of the users who were contacted completed the questionnaires included in the direct message. Of the valid responses, 89.73% (1259/1403) reported suicidal ideation, but more than half (725/1403, 51.67%) reported that they had not sought help. The 9-Item Patient Health Questionnaire (PHQ-9) mean score was 17.40 (SD 5.98). More than two-thirds of the participants (968/1403, 69.00%) thought the PSPO approach was acceptable. Moreover, 2321 users replied to the direct message. In a comparison of the frequency of word usage in their microblog posts 1-month before and after the consultation, we found that the frequency of death-oriented words significantly declined while the frequency of future-oriented words significantly increased.

conclusionsThe PSPO model is suitable for identifying populations that are at risk of suicide. When followed up with proactive crisis management, it may be a useful supplement to existing prevention programs because it has the potential to increase the accessibility of antisuicide information to people with suicidal thoughts and behaviors but a low motivation to seek help.

Indexed as

Suicidal IdeationSuicide PreventionAsian PeopleFemaleHumansMachine LearningMaleSocial MediaSurveys and QuestionnairesChinese young peoplecrisis managementmachine learningmicroblog direct messagesocial networksuicide identification

Identifiers

PMID31344675
PMCPMC6682269
OpenAlexW2932009903

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.