Evidence map›Paper›PMID 36681793›Full record

ArticleBMC pregnancy and childbirth2023

Efforts and expectations of pregnant women against the impact of the COVID-19 pandemic: a phenomenological study.

Arlina Dewi, Triantoro Safaria, Supriyatiningsih Supriyatiningsih, Dyah Tri Kusuma Dewi

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2023. 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
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

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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.

  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.

Arlina DewiDepartment of Public Health, Master of Hospital Administration, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia. arlinadewi@umy.ac.id.
Triantoro SafariaDepartment of Psychology, Faculty of Psychology, Ahmad Dahlan University, Yogyakarta, Indonesia.
Supriyatiningsih SupriyatiningsihDepartment of Obstetrics and Gynecology, Faculty of Medicine and Health Sciences, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia.
Dyah Tri Kusuma DewiDepartment of Public Health, Master of Hospital Administration, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 is a global threat that directly impacts people's mental health and physical well-being. This study explored the efforts and expectations of pregnant women against the impact of the COVID-19 pandemic.

methodsThis study was a qualitative study that used a phenomenological approach. The informants of this study were pregnant women (n = 20). Data analysis used content analysis with software assistance (Nvivo Release 1.5).

resultsThe results of this study identified three themes which were: 1) causative factors of pregnant women's anxiety regarding the impact of COVID-19 including lack of knowledge regarding the impact of the COVID-19 virus and perceived susceptibility; 2) Efforts to reduce anxiety during the COVID-19 pandemic including a spiritual approach, the role of family and COVID-19 prevention; and 3) Expectation regarding healthcare services during COVID-19 including virtual based Antenatal Care (ANC) Services and Private ANC Services.

conclusionA spiritual approach, the role of family, and COVID-19 prevention will help pregnant women reduce their anxiety about being infected with the COVID-19 virus. Furthermore, virtual-based ANC Services, and private ANC services, such as home visits and dividing ANC services and general services into two different tracks as a protective mechanism from being infected with the COVID-19 virus, would assist pregnant women feel safer and secure.

Indexed as

COVID-19Pregnant PeopleFemaleHumansMotivationPandemicsPregnancyPrenatal CareSARS-CoV-2Antenatal careAnxietyVirtual based

Identifiers

PMID36681793
PMCPMC9862243

What OpenQuestion holds

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LicenceCC BY
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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.