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S03.3 Addressing gender-based violence to reduce risk of STI and HIV

2013· article· en· W2319075216 on OpenAlexaboutno aff
A. Amin, Claudia Moreno

Bibliographic record

VenueSexually Transmitted Infections · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGonorrheaDomestic violencePopulationDemographySexual violenceSyphilisSex workChlamydiaEnvironmental healthPoison controlHuman immunodeficiency virus (HIV)Injury preventionImmunology

Abstract

fetched live from OpenAlex

Background Gender-based violence, and gender inequality more broadly, has been found to be associated with increased risk of sexually transmitted infections (STI) including HIV among women and girls as well as among key vulnerable groups such as sex workers. This paper presents the evidence of the increased risk of STI and HIV associated with gender-based violence; and looks at potential pathways by which gender-based violence and STI and HIV are linked. Methods A systematic review and meta-analysis of studies that measure the association between intimate partner violence and STI and HIV was conducted by the London School of Hygiene and Tropical Medicine and WHO as part of work feeding into the Global Burden of Disease Study estimates on violence against women and its health impacts. Another systematic review of studies that measure association between violence against sex workers and STI and HIV was also conducted by the University of British Columbia, Vancouver and WHO. Other studies and literature were reviewed to identify potential pathways to explain the links between gender-based violence and HIV. Results The results of the systematic review show that best estimate of association between physical and/or sexual intimate partner violence and HIV was an odds ratio (OR) of 1.52 (95% CI = 1.03 to 2.23) for HIV, from studies from generalized and concentrated HIV epidemics and slightly higher for syphilis, chlamydia or gonorrhea. . These studies, however, are mainly cross sectional population-based surveys among women in the general population. The systematic review of violence against sex workers shows that sex workers from India and US who experience sexual violence have between 2 and 3-fold increased risk of HIV sero-positivity. Sex workers who experience any form of physical or sexual violence by any perpetrator in studies from India (Karnataka), Thailand, USA (San Francico) also showed increased risk of STI sero-positivity. Studies suggest 4 potential pathways linking gender-based violence and STI/HIV. First, sexual violence can be directly associated with increased STI and HIV transmission. There are also several indirect mechanisms; these include a history of violence in childhood or adolescence being linked to increased sexual risk taking later; and difficulties in negotiation of condom use with the partner. Also, men who perpetrate violence are also more likely to engage in sexual risk taking. Third, fear of violence can prevent women and sex workers from seeking or accessing HIV information and services. Lastly, violence can be an outcome of diagnosis and disclosure of HIV status. Conclusion Interventions to address the HIV epidemic among women and among sex workers need to address violence as a risk factor. In each setting, interventions need to be based on an understanding of the potential pathways that link violence against women and sex workers to STI and HIV infection. HIV prevention, treatment, and care programmes for women and for sex workers can integrate violence prevention into their risk-reduction counselling and communication, work with men and boys to promote gender equality and reduce violence perpetration, empower women, girls and sex workers, address harmful gender norms that perpetuate the acceptability of violence, and address the harmful use of alcohol. Laws and policies that criminalize sex workers and that perpetuate gender-based discrimination against women and girls also need to be addressed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.028
GPT teacher head0.309
Teacher spread0.281 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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