Exploring the Potential of Participatory Theatre to Reduce Stigma and Promote Health Equity for Lesbian, Gay, Bisexual, and Transgender (LGBT) People in Swaziland and Lesotho
Bibliographic record
Abstract
Stigma and discrimination affecting lesbian, gay, bisexual, and transgender (LGBT) people compromise health and human rights and exacerbate the HIV epidemic. Scant research has explored effective LGBT stigma reduction strategies in low- and middle-income countries. We developed and pilot-tested a participatory theatre intervention (PTI) to reduce LGBT stigma in Swaziland and Lesotho, countries with the world's highest HIV prevalence. We collected preliminary data from in-depth interviews with LGBT people in Lesotho and Swaziland to enhance understanding of LGBT stigma. Local LGBT and theatre groups worked with these data to create a 2-hour PTI composed of three skits on LGBT stigma in health care, family, and community settings in Swaziland (Manzini) and Lesotho (Maseru, Mapoteng). Participants ( n = 106; nursing students, health care providers, educators, community members) completed 12 focus groups following the PTI. We conducted thematic analysis to understand reactions to the PTI. Focus groups revealed the PTI increased understanding of LGBT persons and issues, increased empathy, and fostered self-reflection of personal biases. Increased understanding included enhanced awareness of the negative impacts of LGBT stigma, and of LGBT people's lived experiences and issues. Participants discussed changes in attitude and perspective through self-reflection and learning. The format of the theatre performance was described as conducive to learning and preferred over more conventional educational methods. Findings indicate changed attitudes and awareness toward LGBT persons and issues following a PTI in Swaziland and Lesotho. Stigma reduction interventions may help mitigate barriers to HIV prevention, treatment, and care in these settings with a high burden of HIV.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".