Evidence map›Paper›PMID 34961823›Full record

ArticleComputational and mathematical methods in medicine2021

Effects of Personality Characteristics of Different Genders and Quality of Life Analysis on Risk Factors for Suicide in Depressive Patients.

Xuebing Xu, Jing Jiang, Hongjuan Bai, Tao Tian

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 76% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

4 authors at 1 institution in 1 country.

Xuebing XuNingxia Ning-An Hospital, 236 Jinbo South Street, Xixia District, Yinchuan, Ningxia 750021, China.ORCID https://orcid.org/0000-0002-6861-3394
Jing JiangNingxia Ning-An Hospital, 236 Jinbo South Street, Xixia District, Yinchuan, Ningxia 750021, China.
Hongjuan BaiNingxia Ning-An Hospital, 236 Jinbo South Street, Xixia District, Yinchuan, Ningxia 750021, China.
Tao TianNingxia Ning-An Hospital, 236 Jinbo South Street, Xixia District, Yinchuan, Ningxia 750021, China.
Ningxia Hui Autonomous Region Peoples Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe study was to explore the roles of personality characteristics of different genders and analyze the risk factors of quality of life (QOL) analysis in suicide among depressive patients.

methodsOne hundred and eighty-six depressive patients from January 2018 to March 2019 in the Department of Psychiatry of our hospital were enrolled and divided into Groups A and B considering whether they had a suicidal tendency or not. Among them, 90 in Group A had a suicidal tendency and consisted of 42 males and 48 females, while 96 in Group B had no suicidal tendency and consisted of 44 males and 52 females. Forward and backward selection and then backward selection were performed on all the variables of gender characteristic factors and QOL factors that may cause suicide, on which stepwise regression was finally conducted. Next, univariate logistic regression analysis was first performed to select important variables from the related risk factors that may cause suicide, and then, the multivariate logistic regression model was used to select important independent risk factors.

Indexed as

PersonalityAdultAnxietyChinaComputational BiologyDepressive DisorderFemaleHumansLogistic ModelsMaleMultivariate AnalysisQuality of LifeRisk FactorsSex CharacteristicsSuicidal IdeationSuicide

Identifiers

PMID34961823
PMCPMC8710149
OpenAlexW4200332360

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.