Optimal characteristics of peer navigators: adapting peer-based intervention with street-involved youth in Canada and Kenya with the aim of increasing HIV prevention, testing and treatment
Bibliographic record
Abstract
BACKGROUND: We sought to adapt a peer navigator (PN) model to increase uptake of human immunodeficiency virus (HIV) prevention, testing and treatment of street-involved youth (SIY) in Canada and Kenya. This article presents key findings on the optimal characteristics of the PN model for SIY across and between sites, prior to intervention implementation. METHODS: Using an integrated mixed methods approach, eligible participants included SIY aged 16-29 years, healthcare providers and community stakeholders. Data collection tools drew from the CATIE (Canada) PN practice guidelines related to: PN role and responsibilities, training, supervision and integration into sites, among others. During interviews (n = 53) or focus groups (n = 11) with participants, a 39-item PN components checklist was administered (quantitative data), followed immediately by a semi-structured interview protocol with questions that allowed for deeper exploration into the acceptability and appropriateness of the PN intervention (qualitative data). The checklist enabled participants to identify PN characteristics and/or activities as core (essential) or peripheral (adaptable and less important). Spearman's rank correlations (ρ) were used to quantify agreement across sites and participant groups. Qualitative data were inductively coded and analysed using a single codebook. RESULTS: Quantitative data analysis revealed that out of 39 checklist items, 31 (79%) were considered core. These primarily pertained to host organization, PN characteristics and PN activities. For example, it was agreed that core PN activities included outreach to out-of-care SIY and providing health and social service referrals. There were mixed opinions about asking the PN to declare previous experience with drug use and HIV status, but there was agreement that the PN should have previous experience of street-involvement. Qualitative data analysis suggested that although all participant groups across sites agreed that the PN intervention was acceptable and appropriate, the participants from each site also identified specific adaptations related to their host organization and target SIY. CONCLUSIONS: Our findings indicate high agreement among participant groups across all sites on some optimal PN intervention characteristics, particularly host organization characteristics, the PN themselves and their activities. However, context-specific adaptations are necessary to successfully scale-up the PN intervention. This model is applicable in diverse regions and organizational contexts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 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".