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Record W4311156301 · doi:10.1186/s12888-022-04448-3

The relationship between mental health and violence toward women during the COVID-19 pandemic

2022· article· en· W4311156301 on OpenAlexaff
Najmeh Khatoon Shoaei, Neda Asadi, Mahin Salmani

Bibliographic record

VenueBMC Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPandemicMental healthCoronavirus disease 2019 (COVID-19)Domestic violenceGeneral Health QuestionnairePsychiatryPsychologyOccupational safety and healthSuicide preventionHuman factors and ergonomicsInjury preventionMedicinePoison controlClinical psychologyEnvironmental healthDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has a number of psychological consequences for societies, especially women. This study was conducted to determine the relationship between mental health and violence toward women during the COVID-19 pandemic in Iran.This study was conducted on during late October to November 2020 (N = 400). Demographic information questionnaire, General Health Questionnaire(GHQ-12) and violence toward women inventory(VTWI) were used.The results showed that violence was higher among employed women. Also, the results showed that VTW was higher in women with 3 children, high school degree, family income under 3 million and women over 40 years old. Findings showed that the mean mental health of women at the COVID-19 pandemic was moderate (15.14 ± 8.8). Also, with increasing psychological and economic violence, their mental health decreases. Therefore, it is suggested that policy makers and planners, apart from the physical effects of the COVID-19 pandemic, pay attention to its psychological dimension, especially for women, and try to allocate funds to maintain and promote mental health and family.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.133
GPT teacher head0.424
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2022
Admission routes1
Has abstractyes

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