Evidence map›Paper›PMID 34911844›Full record

ArticleZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences2021

Using virtual patient to assess primary health workers

Chao Zhang, Xin Jin, Dan Luo, Dong Xu, Jing Liao, Wenjie Gong

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences, 2021. 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, top 75% 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, 0 citations in OpenAlex.

  1. Article
  2. Observational
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

6 authors at 4 institutions in 3 countries.

Chao ZhangDepartment of Maternal, Child and Adolescent Health, Xiangya School of Public Health, Central South University, Changsha 410005. zhangch@csu.edu.cn.
Xin JinDepartment of Maternal, Child and Adolescent Health, Xiangya School of Public Health, Central South University, Changsha 410005.
Dan LuoDepartment of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410005. luodan_csu_2011@126.com.
Dong XuCenter for World Health Organization Studies and Department of Health Management, School of Health Management, Southern Medical University, Guangzhou 510080.
Jing LiaoGlobal Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou 510080.
Wenjie GongDepartment of Maternal, Child and Adolescent Health, Xiangya School of Public Health, Central South University, Changsha 410005. gongwenjie@csu.edu.cn.
Central South University · CNBirmingham City University · GBSun Yat-sen University · CNWorld Health Organization - Pakistan · PK

Funding

the Major Scientific and Technological Projects of Hunan Province, China 2019SK1011the National Natural Science Foundation of China 81773446the Open Competition of China Medical Board CMB16-260World Health Organization 001
6 · The paper itself

Abstract

objectivesPrimary health workers are the first fine to identify postpartum depression, which is important for patients with this disease to get early specialist diagnosis and treatment. The smartphone-based virtual patient is economical, convenient and effective, and has been applied extensively to evaluate the competency to detect postpartum depression, but there is no relevant application in China. This study aims to use virtual patient to assess the current status on the competency of detecting postpartum depression among primary maternal and child health workers in Hunan Province, and to explore potential influencing factors.

methodsA total of 222 primary maternal and child health workers from 3 regions with low, medium, and high economic levels in Hunan Province were enrolled, and smartphone-based virtual patients with postpartum depression were used for the assessment from May to July in 2018, and a self-designed questionnaire was used to investigate their demographic characteristics. The competency to detect postpartum depression was measured by 2 indicators: diagnostic accuracy and treatment accuracy. Descriptive statistical methods were used to describe the competency to detect postpartum depression among them and their demographic characteristics. A logistic regression analysis was used to explore the possible influencing factors for the diagnostic accuracy and treatment accuracy.

resultsThe diagnostic accuracy rate was 64.0%. There was no significant difference between the demographic characteristics and diagnostic accuracy rate (

conclusionsAbout half of the primary maternal and child health workers in Hunan Province, China have basic competency to detect postpartum depression, but the overall results are not satisfactory. The regional economic level is correlated with the competency of detecting postpartum depression, and the competency of detecting postpartum depression is stronger in more developed areas. Moreover, for the patients who have been identified as postpartum depression, the rate of correct treatment is low, which warrants particular attention in the follow-up training.

Indexed as

Depression, PostpartumChildChinaCross-Sectional StudiesFemaleHealth PersonnelHumansSurveys and Questionnairescompetencypostpartum depressionrecognitionvirtual patient

Identifiers

PMID34911844
PMCPMC10930235
OpenAlexW4200010869

What OpenQuestion holds

Textmetadata
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.