What do we know about interventions to reduce intersectional stigma and discrimination in the context of HIV? A systematic review.
Bibliographic record
Abstract
There is ample literature on interventions to reduce human immunodeficiency virus (HIV) stigma and discrimination and extant theory around intersectionality. However, the integration of intersectionality into the design and implementation of stigma reduction interventions is nascent. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we reviewed 23 studies from six countries to examine the state of the evidence on interventions to reduce intersectional stigma in the context of HIV. Thirteen studies made explicit reference to intersectionality, 11 of which addressed all three stigma domains: drivers, facilitators, and manifestations. Most interventions were multilevel and multistrategy, yet only five included a structural component. Thirteen studies focused on four or more intersections (e.g., HIV, race, sexual identity, gender), five on three intersections, and five on two intersections. Twenty studies (87%) reported medium (n = 5) to high (n = 15) community engagement. The majority of studies (19/23) assessed HIV-related (e.g., antiretroviral therapy [ART] adherence) and/or empowerment-based outcomes (e.g., self-esteem, coping), with 91% reporting some positive intervention effects. Of 13 studies that measured stigma outcomes, only seven (54%) documented some improvement in the stigma measures assessed. Our review revealed a range of sophisticated, intersectionality-informed interventions that were mostly successful at improving HIV, sexual health, and empowerment-based outcomes, but less successful at reducing the aspects of stigma measured. Future research should encompass wider geographical regions, use validated measures of intersectional stigma, and test structural interventions and approaches that challenge the systems of power and oppression that fuel stigma, inequality, and poor health outcomes among multiply marginalized populations.
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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 | 0.014 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".