Resisting and disrupting HIV-related stigma: a photovoice study
Bibliographic record
Abstract
BACKGROUND: The stigma associated with human immunodeficiency virus (HIV) is a significant global public health concern. Health care providers and policy makers continue to struggle with understanding and implementing strategies to reduce HIV-related stigma in particular contexts and at the intersections of additional oppressions. Perspectives and direction from people living with HIV are imperative. METHODS: In this project we amplified the voices of people living with HIV about their experiences of HIV-related stigma in Manitoba, Canada. We used an arts-based qualitative case study research design using photovoice and narrative interviews. Adults living with HIV participated by taking pictures that represented their stigma experiences. The photos were a catalyst for conversations about HIV and stigma during follow-up individual narrative interviews. Journaling provided opportunities for participants to reflect on their experiences of, and resistance to, stigma. Interviews were audio recorded and transcribed. Photos, journals, and transcribed interviews were analyzed using inductive qualitative methods RESULTS: Through pictures and dialogue, participants (N = 11; 64% women) expressed the emotional and social impacts of stigmas that were created and supported by oppressive structures and interpersonal attitudes and behaviours. These experiences were compounded by intersecting forms of oppression including racism, sexism, and homophobia. Participants also relayed stories of their personal strategies and transitions toward confronting stigma. Strategies were themed as caring for oneself, caring for children and pets, reconstituting social support networks, and resisting and disrupting stigma. Participants made important recommendations for system and policy change. CONCLUSIONS: These stories of oppression and resistance can inspire action to reduce HIV-related stigma. People living with HIV can consider the strategies to confront stigma that were shared in these stories. Health care providers and policy makers can take concerted actions to support peoples' transitions to resisting stigmas. They can facilitate supportive and anti-oppressive health and social service systems that address medical care as well as basic needs for food, shelter, income, and positive social and community connections.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".