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Record W4390910801 · doi:10.1186/s40621-024-00483-8

Economic hardship and perpetration of intimate partner violence by young men in South Africa during the COVID-19 pandemic (2021–2022): a cross-sectional study

2024· article· en· W4390910801 on OpenAlexafffund
Campion Zharima, Kalysha Closson, Mags Beksinska, Bongiwe Zulu, Julie Jesson, Tatiana Pakhomova, Erica Dong, Janan Dietrich, Angela Kaida, C. Andrew Basham

Bibliographic record

VenueInjury Epidemiology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWomen's Health Research InstituteSimon Fraser UniversityBC Children's Hospital
FundersDivision of Research Capacity DevelopmentMitacs
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Cross-sectional study2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Public healthBiostatisticsMedicineOccupational safety and healthEnvironmental healthPoison controlSuicide preventionEpidemiologyInjury preventionHuman factors and ergonomicsDomestic violenceVirologyNursingOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Economic hardship is a potential trigger for intimate partner violence (IPV) perpetration. While higher IPV rates have been reported in low-income regions, few African studies have focused on IPV being triggered by economic hardship among young men during the COVID-19 pandemic. We therefore estimated economic hardship's effect on IPV perpetration by young men in eThekwini District, South Africa, during the COVID-19 pandemic. METHODS: A cross-sectional survey of COVID-19 pandemic experiences was conducted among youth aged 16-24 years through an anonymous self-administered questionnaire, including questions about economic hardship (increased difficulty accessing food or decreased income) and IPV perpetration. A prespecified statistical analysis plan with a directed acyclic graph of assumed exposure, outcome, and confounder relationships guided our analyses. We measured association of economic hardship and IPV perpetration through odds ratios (ORs) computed from a multivariable logistic regressions adjusted for measured confounders. Secondary outcomes of physical and sexual IPV perpetration were analyzed separately using the same specifications. Propensity score matching weights (PS-MW) were used in sensitivity analyses. Analysis code repository: https://github.com/CAndrewBasham/Economic_Hardship_IPV_perpetration/ RESULTS: Among 592 participants, 12.5% reported perpetrating IPV, 67.6% of whom reported economic hardship, compared with 45.6% of those not reporting IPV perpetration (crude OR = 2.49). Median age was 22 years (interquartile range 20-24). Most (80%) were in a relationship and living together. Three quarters identified as Black, 92.1% were heterosexual, and half had monthly household income < R1600. We estimated an effect of economic hardship on the odds of perpetrating IPV as OR = 1.83 (CI 0.98-3.47) for IPV perpetration overall, OR = 6.99 (CI 1.85-36.59) for sexual IPV perpetration, and OR = 1.34 (CI 0.69-2.63) for physical IPV perpetration. PS-MW-weighted ORs for IPV perpetration by economic hardship were 1.57 (overall), 4.45 (sexual), and 1.26 (physical). CONCLUSION: We estimated 83% higher odds of self-reported IPV perpetration by self-reported economic hardship among young South African men during the COVID-19 pandemic. The odds of sexual IPV perpetration were The seven-times higher by economic hardship, although with limited precision. Among young men in South Africa, economic hardship during COVID-19 was associated with IPV perpetration by men. Our findings warrant culturally relevant and youth-oriented interventions among young men to reduce the likelihood of IPV perpetration should they experience economic hardship. Further research into possible causal mechanisms between economic hardship and IPV perpetration could inform public health measures in future pandemic emergencies.

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)0.002
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 score0.008
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.408
Teacher spread0.340 · 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

Citations12
Published2024
Admission routes2
Has abstractyes

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