Sexy Health Carnival on the Powwow Trail: HIV Prevention by and for Indigenous Youth
Bibliographic record
Abstract
Background: This article introduces a peer-led pilot intervention called the “Sexy Health Carnival” (SHC) that takes a strengths-based approach to promoting Indigenous youth sexual health in a culturally safe context. Methods: In 2014, Indigenous youth leaders brought the SHC to 4 Ontario, Canada, powwows, where they administered an offline iPad survey to 154 Indigenous youth (aged 16 to 25) who engaged with the SHC. The survey gathered descriptive data on HIV prevention behaviours and intentions, and the acceptability of the SHC approach in powwow settings. Results: Over one third (40%) of youth thought that “a lot” of sex happens at powwows; 14% reported that they were either “definitely” or “probably” going to “hook up” or be sexual with someone at the powwow, and another 14% were not sure. Among those contemplating sexual activity, 79% said they would use a condom that they received at the SHC. The majority (80%) of youth rated the SHC as “awesome.” Conclusion: This pilot provides preliminary evidence that the SHC is feasible and welcomed by youth in powwow settings. This project illustrates that Indigenous youth are capable of developing successful sexual health outreach and HIV prevention resources for each other.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".