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Record W2958664933 · doi:10.1177/1077801219856101

Exploring the Relationship Between Stigma, Stigma Challenges, and Disclosure Among Slum-Dwelling Survivors of Intimate Partner Violence in Kenya

2019· article· en· W2958664933 on OpenAlexafffund
Eleanor Maticka‐Tyndale, Jessica Penwell Barnett, Trocaire

Bibliographic record

VenueViolence Against Women · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of Windsor
FundersCanada Research ChairsIrish Aid
KeywordsStigma (botany)Domestic violenceEmpowermentSlumSelf-disclosurePoison controlSuicide preventionInterpersonal communicationPsychologyMedicineClinical psychologySocial psychologyPsychiatryEnvironmental healthPopulationPolitical science

Abstract

fetched live from OpenAlex

This article uses survey data from 131 women living in urban slums in Kenya to explore associations between stigma, stigma challenges, empowerment, and disclosure of intimate partner violence (IPV). A total of 81.7% of women reported informal or formal disclosure of IPV. A bystander offering help and experiencing stigma were associated with significant increases in the odds of informal and formal disclosure. There were also significant positive associations between participating in financial decision-making, membership in survivor support groups, and formal disclosure. Results suggest that interpersonal, community, and structural challenges to stigma interfere with stigma as a barrier to disclosure.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.301
Teacher spread0.216 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

Explore more

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