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Record W4205789619 · doi:10.3390/jcm11020349

Elevated Perinatal Depression during the COVID-19 Pandemic: A National Study among Jewish and Arab Women in Israel

2022· article· en· W4205789619 on OpenAlexaff
Samira Alfayumi‐Zeadna, Rena Bina, Drorit Levy, Rachel Merzbach, Atif Zeadna

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsnot available
FundersNational Cancer Institute
KeywordsMedicinePublic healthJudaismAnxietyPopulationDepression (economics)DemographyPandemicMental healthPsychiatryPregnancyCoronavirus disease 2019 (COVID-19)Environmental healthNursingInternal medicine

Abstract

fetched live from OpenAlex

This study assessed prevalence of perinatal depression symptoms (PNDS) during the COVID-19 pandemic among Arab and Jewish women in Israel and identified COVID-19-related risk factors for PNDS, while comparing Arab and Jewish women. Sample included 730 perinatal women (604 Jewish and 126 Arab) aged 19-45 years, who filled out an online self-report questionnaire. The questionnaire assessed several areas: perinatal experiences and exposure to COVID-19, social support, and financial and emotional impact. PNDS was measured by the Edinburgh Postnatal Depression Scale (EPDS). Prevalence of PNDS (EPDS ≥ 10) in the entire study population was 40.0%. Prevalence among Arab women was significantly higher compared to Jewish women (58% vs. 36%, PV < 0.001). Higher PNDS were significantly associated with anxiety symptoms (GAD ≥ 10) (PV < 0.001), stress related to COVID-19 (PV < 0.001), adverse change in delivery of healthcare services (PV = 0.025), and unemployment (PV = 0.002). PNDS has elevated more than twofold during COVID-19 in Israel. Such high rates of PNDS may potentially negatively impact women, and fetal and child health development. This situation requires special attention from public health services and policy makers to provide support and mitigation strategies for pregnant and postpartum women in times of health crises.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.093
GPT teacher head0.442
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2022
Admission routes1
Has abstractyes

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