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Record W4385388715 · doi:10.1080/13691058.2023.2236163

Experiences of gender-based violence among Somali refugee women: a socio-ecological model approach

2023· review· en· W4385388715 on OpenAlexaff
Comfort Tosin Adebayo, Oluwatoyin Olukotun, Mary Olukotun, Jackline Kirungi, Kaboni Whitney Gondwe, Natasha Crooks, Randi Singer, Shukri Adams, Faten Yahya Alfaifi, Anne Dressel, Laila Fahmy, Peninah Kako, Julia Snethen, Lucy Mkandawire‐Valhmu

Bibliographic record

VenueCulture Health & Sexuality · 2023
Typereview
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of Alberta
FundersNational Institute on Minority Health and Health Disparities
KeywordsSomaliRefugeePopulationSexual violenceSocial ecological modelDomestic violenceResidenceHealth carePsychologySociologyCriminologyPoison controlGender studiesMedicineSuicide preventionPolitical scienceEnvironmental healthDemography

Abstract

fetched live from OpenAlex

Gender-based violence (GBV) is an all-encompassing term that speaks to acts or threats that may lead to physical, sexual or emotional harm to an individual based on their gender. This paper provides a scoping review of research on gender-based violence among Somali refugee women in different parts of the world. Using the socio-ecological model as a framework, we reviewed 30 empirical studies focusing on some form of GBV among Somali refugee women. We identified societal, community and individual factors contributing to the experience of GBV. We also discuss how these factors influence women's willingness to access care, especially healthcare and social services. The review reveals that oftentimes, institutions that work closely with this population have a limited understanding of how closely culture affects the willingness and ability to seek help about GBV. Based on our analysis, we suggest ways in which social institutions and healthcare providers can provide culturally-safe support to Somali refugee women who have experienced some form of GBV.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.253
GPT teacher head0.455
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 designQualitative
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

Citations7
Published2023
Admission routes1
Has abstractyes

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