A Critical Review of the Characteristics of Theater-Based HIV Prevention Interventions for Adolescents in School Settings
Bibliographic record
Abstract
Theater-based interventions are a viable prevention strategy for changing sexual health knowledge, attitudes, and behaviors related to HIV prevention. However, few studies have explored interventions in English-speaking, high-income countries such as the United States, Canada, or the United Kingdom. This article critically reviews the literature to identify key characteristics of theater-based HIV prevention strategies used for adolescents in school-settings in the United States, Canada, and the United Kingdom. Specifically, we identify the theatrical approach used in HIV prevention interventions, the behavioral theories that inform such interventions, and the study design and results of existing evaluation studies conducted in school settings. In the 10 articles reviewed, we found limited grounding in theory and the use of nonrigorous study design. To strengthen the evidence and practical application of theater-based HIV prevention interventions, we highlight three specific recommendations for practitioners and researchers: (1) define and operationalize the theater approach and techniques used, (2) ensure theater-based interventions are grounded in theory, and (3) conduct rigorous evaluation of theater-based interventions. These recommendations are key to strengthening future research on and implementation of theater-based interventions for HIV prevention.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".