MétaCan
Menu
← Back to cohort
Record W4404325209 · doi:10.1186/s12889-024-20439-3

Stigma trajectories, disclosure, access to care, and peer-based supports among African, Caribbean, and Black im/migrant women living with HIV in Canada: findings from a cohort of women living with HIV in Metro Vancouver, Canada

2024· article· en· W4404325209 on OpenAlexafffundabout
Faaria Samnani, Kathleen Deering, Desire King, Patience Magagula, Melissa Braschel, Kate Shannon, Andrea Krüsi

Bibliographic record

VenueBMC Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsSimon Fraser UniversityBritish Columbia Centre of Excellence for Women's HealthUniversity of British Columbia
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchNational Institutes of HealthCanadian HIV Trials Network, Canadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsMedicineStigma (botany)GerontologyDemographyPublic healthGeeCohortBiostatisticsSocial supportGeneralized estimating equationPsychologySocial psychologyPsychiatrySociologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: African, Caribbean, and Black im/migrant women experience a disproportionate burden of HIV relative to people born in Canada, yet there is scarce empirical evidence about the social and structural barriers that influence access to HIV care. The objectives of this study is to estimate associations between African, Caribbean, and Black background and stigma and non-consensual HIV disclosure outcomes, and to understand how experiences of stigma and im/migration trajectories shape access to HIV care and peer supports among African, Caribbean, and Black im/migrant women living with HIV in Canada. METHODS: This mixed-methods analysis draws on interviewer-administered questionnaires and semi-structured interviews with self-identifying African, Caribbean, and Black women living with HIV in the community-based SHAWNA (Sexual Health and HIV/AIDS: Women's Longitudinal Needs Assessment) cohort. Bivariate and multivariable logistic regression using generalized estimating equations (GEE) were performed to estimate associations between African, Caribbean, and Black background and stigma and non-consensual HIV disclosure outcomes. Drawing on a social and structural determinants of health framework, qualitative analysis of interviews elucidated the interplay between migration trajectories, stigma, racialization, and HIV. RESULTS: Amongst our participants (n = 291), multivariable GEE analysis revealed that African, Caribbean, and Black participants (n = 15) had significantly higher odds of recently being outed without consent as living with HIV (AOR 2.34, 95% CI 0.98-5.57). Additionally, African, Caribbean, and Black participants had higher odds of recent verbal or physical abuse due to their HIV status (AOR 2.11, 95% CI 0.65-6.91). Reflecting on their im/migration trajectories, participants' narratives (n = 9) highlighted experiences of political violence and conflict, trauma, stigma, and discrimination associated with HIV in their place of origin and the racialization and stigmatization of HIV in Canada. Fear of disclosure without consent was linked to barriers of accessing care and peer-based supports. CONCLUSION: Our findings indicate that im/migration trajectories of African, Caribbean, and Black women living with HIV are critically related to accessing HIV care and supports in Canada and compound HIV stigma and discrimination. HIV disclosure without consent complicates access to care and social/peer support, underscoring the need for privacy, confidentiality, and the importance of building trust in the context of clinical encounters. The results of this study emphasize the critical need for culturally sensitive trauma-informed care models rooted in peer-based approaches.

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)0.002
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 score0.017
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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

Citations7
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueBMC Public Health→Same topicHIV/AIDS Research and Interventions→French-language works237,207→