Evidence map›Paper›PMID 35174122›Full record

ArticleFrontiers in public health

Impact of the COVID-19 Lockdown on Quality of Life in Pregnant Women.

Jingjie Ma, Ating Wang, Hang Zhou

Abstract read
In one paragraph

Article in Frontiers in public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

6 citing papers in PubMed.

  1. Trial
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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

3 authors.

Jingjie MaDepartment of Nutrition, Baoji Maternal and Child Health Hospital, Baoji, China.
Ating WangDepartment of Nutrition, Baoji Maternal and Child Health Hospital, Baoji, China.
Hang ZhouClinical Medical College, Yangzhou University, Yangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has been associated with significant impacts on mental health and well-being of populations worldwide. However, little is known about its significant impact on psychological aspects of vulnerable population groups such as pregnant women. Therefore, the aim of the study was to determine the psychological impact of the COVID-19 pandemic among pregnant women in mainland China.

methodsA cross-sectional survey was performed between July and August 2020 using a modified validated 40-item questionnaire which consisted of sociodemographics, attitude, lifestyle changes and the Impact of Event Scale (IES) toward COVID-19 using snowball and convenience samplings.

resultsA total of 1,078 participants were included in the study. The mean age of participants was 29.4 ± 4.0 years. Overall, the mean IES of participants was 30.6 ± 12.8 (i.e., moderate-to-severe stressful impact) amidst the COVID-19 pandemic, with 63.9% of participants had an IES score ≥26. Despite increased family and social support, more than half of participants also reported increased feeling of being horrified, apprehensive and helpless.

conclusionsThe COVID-19 pandemic has several psychological impacts on pregnant women. Therefore, based on these valuable data of pregnant women collected, we recommend that a thoughtful planning and time preparation by the government would definitely help to reduce the negative impacts caused by the COVID-19 pandemic and restore the quality of life among pregnant women. Further research is needed to identify vulnerable groups including pregnant women to better adapt and inform mental health interventions and policies by health authorities.

Indexed as

COVID-19Quality of LifeAdultCommunicable Disease ControlCross-Sectional StudiesFemaleHumansPandemicsPregnancyPregnant PeopleSARS-CoV-2ChinaIESpregnant womenpsychiatrystress

Identifiers

PMID35174122
PMCPMC8841653

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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.