Using scenario videos with Theatre Testing method to adapt a peer navigation model to improve street-connected youth's access to HIV care in Kenya and Canada
Bibliographic record
Abstract
Theatre testing (TT) method demonstrates whole or portions of an evidence-based intervention to stakeholders to elicit feedback on context-specific adaptations and future implementation. The Peer Navigator Project (PNP) studied the adaptation and implementation of Peer Navigators in five urban sites to increase street-connected youth (SCY) access to HIV prevention, testing, and treatment in Canada and Kenya. TT was used with SCY, healthcare providers, and community stakeholders to collect feedback on the optimal characteristics of the PNs (e.g., social identities) and their professional activities and responsibilities in each site. Sites scripted unique scenarios of PNs supporting SCY and interacting with social service providers. Local actors were employed, and the scenarios were filmed and edited into videos alongside audience discussion questions. Videos were screened to separate audiences of SCY ( n = 40), healthcare providers ( n = 12), and community stakeholders ( n = 59). Facilitated discussion about the scenarios were recorded as data, and transcripts were analyzed thematically by the research team. The scenario videos are presented as a unique adaptation to the TT method. The adaptations were time-consuming and limited the ability to present responsive changes while presenting the method to different audiences. They were also effective at maintaining presentation fidelity and eliciting diverse and meaningful responses from different stakeholder groups. One site successfully adapted the method for use in a physically distanced manner that complied with COVID-19 public health regulations. TT using video scenarios is an engaging approach that garners rich responses from diverse stakeholder groups about the adaptation of evidence-based interventions preparing for implementation in international settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".