Clearing Space for Multiple Voices: HIV Vulnerability amongst South Asian Immigrant Women in Toronto
Bibliographic record
Abstract
This paper shares findings from a community-based research study conducted with South Asian women living with HIV in Toronto. Using qualitative methods, specifically in-depth interviews, participants’ experiences contribute to the creation of a more nuanced and intersectional understanding of HIV risk and support. Their narratives highlighted specific vulnerabilities growing out of structural inequities and gender-based power imbalances in their families and with their sexual and/or marital partners. The participants’ insights have important social justice and health program development implications. Résumé Cet article partage les conclusions d’une étude de recherche communautaire menée auprès de femmes d’Asie du sud vivant avec le VIH à Toronto. À l’aide de méthodes qualitatives, en particulier d’entretiens approfondis, les expériences des participantes contribuent à l’émergence d’une compréhension plus nuancée et intersectionnelle du risque de VIH et du soutien aux personnes atteintes du VIH. Leurs récits ont mis en évidence des vulnérabilités spécifiques découlant d’inégalités structurelles et de déséquilibres de pouvoir fondés sur le sexe dans leur famille et avec leurs partenaires sexuels ou conjugaux. Les révélations des participantes ont d’importantes répercussions en matière de justice sociale et de développement des programmes de santé.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".