Effects Of HIV stigma reduction interventions in diasporic communities: insights from the CHAMP study
Bibliographic record
Abstract
Racialized diasporic communities in Canada experience disproportionate burden of HIV infection. Their increased vulnerabilities are associated with interlocking challenges, including barriers in accessing resources, migration and settlement stress, and systemic exclusion. Further, people living with HIV (PLHIV) in these diasporic communities face stigma and discrimination in both mainstream Canadian society as well as their own ethno-racial communities. HIV stigma negatively impacts all aspects of HIV care, from testing to disclosure to treatment and ongoing care. In response to these challenges, a Toronto based community organization developed and implemented the CHAMP project to engage people living with HIV/AIDS (PLHIV) and leaders from different service sectors from the African/Caribbean, Asian and Latino communities to explore challenges and strategies to reduce HIV stigma and build community resilience. The study engaged 66 PLHIV and ethno-racial leaders from faith, media and social justice sectors in two stigma-reduction training programs: Acceptance Commitment Therapy Training (ACT) and Social Justice Capacity Building (SJCB). Data collection included pre-and post- intervention surveys, focus groups and monthly activity logs. Participants were followed for a year and data on changes in the participants' attitudes and behaviors as well as their actual engagement in HIV prevention, PLHIV support and stigma reduction activities were collected. CHAMP results showed that the interventions were effective in reducing HIV stigma and increasing participants' readiness to take action towards positive social change. Participants' activity logs over a period of 9 months after completing the training showed they had engaged in 1090 championship activities to advocate for HIV related health equity and social justice issues affecting racialized and newcomer PLHIV and communities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".