Barriers to and facilitators of accessing HIV services for street-involved youth in Canada and Kenya
Bibliographic record
Abstract
INTRODUCTION: UNICEF estimates that there are as many as 100 million street-involved youth (SIY) globally. Marginalized conditions put SIY at higher risk of HIV and adverse outcomes once HIV-positive. The objective of this analysis was to describe barriers and facilitators of accessing HIV prevention, testing, and treatment services as Phase I of an implementation study evaluating the use of peer navigators to increase access to HIV services. METHODS: Semi-structured interviews, focus group discussions (FGD), and theatre testing were conducted with individuals who identify as SIY, health care providers, and community stakeholders living in Canada (Toronto, Montreal, London) and Kenya (Eldoret, Huruma, Kitale). Data were analyzed using a directed content approach, guided by the socio-ecological model (SEM). RESULTS: Across the six sites were 195 participants: 64 SIY, 42 healthcare providers, and 97 community-based stakeholders. Barriers were identified at the societal (e.g. intersectional stigma and discrimination), public policy (e.g., inadequate access to basic needs, legal documentation, lack of health insurance, and limited community-based funding), institutional (e.g. lack of inclusive education and training, inadequate HIV educational outreach, and restrictive service provision), interpersonal (e.g., ineffective communication from healthcare providers), and intrapersonal levels (e.g. lack of trust and associated fear, low perception for healthcare, and lack of self-esteem). These contributed to limited HIV services utilization among SIY. Conversely, numerous facilitators were also identified at the public policy (e.g. affordable HIV services and treatment), institutional (e.g. available and accessible HIV prevention tools, HIV education and awareness programs, and holistic models of care), interpersonal level (e.g., systems navigation support, peer support, and personal relationships), and intrapersonal levels (e.g. self-efficacy) as positively supporting SIY access to HIV services. CONCLUSION: Intersectional stigma was a critical barrier in all sites, and policies and programs that foster welcoming environments for youth from diverse backgrounds and living circumstances may be better able to respond to the HIV service needs of this high risk population. Social support and navigation services were reported to facilitate access to HIV services in all sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".