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Record W2226419164 · doi:10.4038/cmj.v60i4.8100

Intimate partner violence in Sri Lanka: a scoping review

2016· review· en· W2226419164 on OpenAlexafffund
Sepali Guruge, Vathsala Jayasuriya-Illesinghe, Nalika Gunawardena, Jennifer Perera

Bibliographic record

VenueCeylon Medical Journal · 2016
Typereview
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsToronto Metropolitan University
FundersInternational Development Research Centre
KeywordsDomestic violenceMedicineContext (archaeology)Sri lankaCeylonPoison controlSexual violenceSocioeconomicsSuicide preventionEconomic growthEnvironmental healthNursingGeographyTanzaniaSociology

Abstract

fetched live from OpenAlex

South Asia is considered to have a high prevalence of intimate partner violence (IPV) against women. Therefore the World Health Organisation has called for context-specific information about IPV from different regions. A scoping review of published and gray literature over the last 35 years was conducted using Arksey and O'Malley's framework. Reported prevalence of IPV in Sri Lanka ranged from 20-72%, with recent reports of rates ranging from 25- 35%. Most research about IPV has been conducted in a few provinces and is based on the experience of legally married women. Individual, family, and societal risk factors for IPV have been studied, but their complex relationships have not been comprehensively investigated. Health consequences of IPV have been reported, with particular attention to physical health, but women are likely to underreport sexual violence. Women seek support mainly from informal networks, with only a few visiting agencies to obtain help. Little research has focused on health sector responses to IPV and their effectiveness. More research is needed on how to challenge gendered perceptions about IPV. Researchers should capture the experience of women in dating/cohabiting relationships and women in vulnerable sectors (post-conflict areas and rural areas), and assess how to effectively provide services to them. A critical evaluation of existing services and programmes is also needed to advance evidence informed programme and policy changes in Sri Lanka.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.075
GPT teacher head0.463
Teacher spread0.388 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations53
Published2016
Admission routes2
Has abstractyes

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