Bridging the silos in HIV and Hepatitis C prevention: a cross-provincial qualitative study
Bibliographic record
Abstract
OBJECTIVES: The Our Youth Our Response (OYOR) study explored the scope and accessibility of existing youth-oriented human immunodeficiency virus (HIV) and Hepatitis C (HCV) prevention in Atlantic Canada. METHODS: A cross-provincial, qualitative population health and gender-based analytic approach was used in this study. Four hundred and twenty-five documents were part of the initial scoping review, while 47 in-depth interviews across youth-relevant sectors were undertaken to explore the perceptions related to current approaches to youth-oriented HIV/HCV prevention policies and programs. The study also conducted focus group discussions with 21 key informants aimed at identifying strategies to address the challenges identified from the interview data. RESULTS: Five overarching themes emerged from our triangulated data in relation to the present state of youth-related HIV/HCV prevention. These included: inter-organizational and intersectoral collaboration; youth engagement; access to testing; harm reduction; and education. CONCLUSIONS: Our findings will assist in informing the next generation for HIV/HCV prevention aimed at youth. Specifically, the results indicate that future prevention initiatives should support the use of intersectoral collaboration, gender-based approaches, and HIV/HCV testing innovation to help de-stigmatize prevention efforts.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".