Evidence map›Paper›PMID 38701039›Full record

ArticlePloS one2024

Maternal distress during the COVID-19 outbreak: A socio-ecological perspective.

Mor Keleynikov, Noga Cohen, Joy Benatov

Abstract read
In one paragraph

Article in PloS one, 2024. 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
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.

  1. Article
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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.

Mor KeleynikovDepartment of Special Education, Faculty of Education, University of Haifa, Haifa, Israel.ORCID 0000-0001-8550-6542
Noga CohenDepartment of Special Education, Faculty of Education, University of Haifa, Haifa, Israel.ORCID 0000-0002-7682-0289
Joy BenatovDepartment of Special Education, Faculty of Education, University of Haifa, Haifa, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMothers faced an increased risk of adverse mental health outcomes during the COVID-19 pandemic compared to other populations. However, there is little data on the factors that placed mothers at increased risk of distress.

aimsThe present study explored a range of individual, familial, and environmental factors associated with psychological distress in mothers during the COVID-19 pandemic.

methodThis repeated cross-sectional study was composed of a convenience sample of mothers who completed an online survey that included a demographic questionnaire, an emotion regulation questionnaire, and the Depression, Anxiety, and Stress scale. The survey was administered during the second and third lockdowns in Israel in 2020-2021.

resultsThe study included 575 mothers (M age = 39). The findings of a hierarchical regression indicated that individual-level factors, composed of age and emotion regulation tendencies predicted psychological distress. The family-level factors of household income and number of children in the family also predicted distress. In terms of environmental-level factors, COVID-19-related media consumption and school status (open or closed) were also significant predictors of psychological distress. Importantly, the results showed that the most important predictors of psychological distress in mothers during the COVID-19 outbreak were school closures, household income, and the use of adaptive and maladaptive emotion regulation strategies.

conclusionsThe findings highlight the intersection of individual, familial, and environmental factors in mothers' mental health during crises.

Indexed as

COVID-19MothersPandemicsPsychological DistressStress, PsychologicalAdultAnxietyCross-Sectional StudiesFemaleHumansIsraelPregnancy

Identifiers

PMID38701039
PMCPMC11068201

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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.