MétaCan
Menu
Back to cohort
Record W4391944058 · doi:10.4103/ipj.ipj_167_23

Violence against women during pregnancy and its dimensions in COVID-19 pandemic: A systematic review and meta-analysis

2024· review· en· W4391944058 on OpenAlexaboutno aff
Soodabeh Aghababaei, Seyedeh Zahra Masoumi, Reza Tahmasebi, Ensiyeh Jenabi, Zahra Toosi, Samereh Ghelichkhani

Bibliographic record

VenueIndustrial Psychiatry Journal · 2024
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMeta-analysis2019-20 coronavirus outbreakPregnancySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineVirologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

This systematic review and meta-analysis investigated the prevalence of violence against pregnant women during COVID-19 pandemic based on the available evidence. Medline, Scopus, Web of Science, and Google Scholar were searched. All published observational articles from December 2019 to December 2022 were assessed by two independent authors using the "violence, pregnancy, COVID-19" keywords. The quality appraisal of primary studies conducted using the Newcastle - Ottawa Quality Assessment Scale checklist and 10 eligible articles were included in this review. After reviewing the articles, the prevalence of violence among pregnant women during the COVID-19 pandemic was estimated to be 23% [95% confidence interval (CI) =18 to 29%] using the random effect model. Of them, 59% (95% CI = 13 to 105%) was attributed to verbal-behavioral violence, 30% (95% CI = 17 to 42%) emotional violence, 14% (95% CI = 8 to 20%) sexual violence, and 11% physical violence (95% CI = 6 to 17%). The results indicated that the violence prevalence among pregnant women was not different during and before the start of the COVID-19 pandemic. However, the behavioral-verbal, emotional, physical, and sexual violence were the most common forms of violence.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.507
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.234
GPT teacher head0.436
Teacher spread0.202 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial Psychiatry JournalSame topicCOVID-19 Impact on ReproductionFrench-language works237,207