Evidence map›Paper›PMID 35958640›Full record

ArticleFrontiers in psychiatry2022

Trends in online searching toward suicide pre-, during, and post the first wave of COVID-19 outbreak in China.

Hongguang Chen, Konglai Zhang, Hui Li, Mengqian Li, Shunfei Li

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 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
1.2field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  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

5 authors at 5 institutions in 1 country.

Hongguang ChenPeking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
Konglai ZhangDepartment of Epidemiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China.
Hui LiDepartment of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Mengqian LiDepartment of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Shunfei LiChinese PLA General Hospital, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNChinese PLA General Hospital · CNFirst Affiliated Hospital of Nanchang University · CNNanchang University · CNPeking University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19 may increase the risk of suicide, but the conclusion is still unclear. This study was designed to assess the impact of COVID-19 on suicide pre-, during, and post the first wave of COVID-19 in China. It was reported that online public searching was associated with their offline thoughts and behaviors. Therefore, this study was designed to explore the online search for suicide pre-, during, and post-COVID-19 in China. The keywords on suicide, COVID-19, unemployment, and depression were collected in 2019 and 2020 using the Baidu Search Index (BSI). A time-series analysis examined the dynamic correlations between BSI-COVID-19 and BSI-suicide. A generalized estimating equation model was used to calculate the coefficients of variables associated with the BSI-suicide. The BSI-suicide showed a significant increase (15.6%,

Indexed as

COVID-19depressiononline searching behaviorsocial psychiatrysuicide

Identifiers

PMID35958640
PMCPMC9357924
OpenAlexW4287147527

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.