“If you find that I am HIV positive, don’t tell me”: Exploring the barriers and recommendations for HIV prevention services utilization among youth in rural southwestern Uganda
Bibliographic record
Abstract
Globally, the majority of new HIV infections are recorded in Eastern and Southern Africa, with the youth being disproportionately affected. HIV prevention is the cornerstone of controlling the spread of HIV and ending this epidemic by 2030. However, barriers to the utilization of HIV prevention services remained underexplored especially among the youth in rural settings in sub-Saharan Africa. This qualitative study, conducted between February and April 2022 in rural southwestern Uganda, explored these barriers and identified recommendations to improve the utilization of HIV prevention services among youth. We conducted six focus group discussions (with youth [15-24 years] both in and out of school), nine in-depth interviews (with teachers, health workers, and members of the village health team), and four key informant interviews (with district officials) to collect data. Thematic analysis revealed barriers at the individual level (e.g., misconceptions, fear of testing, low perceived HIV risk, confidentiality concerns), community level (e.g., stigma, lack of counseling, peer influence), and health system level (e.g., lack of youth-friendly services). Recommendations included formation of youth peer support groups, ongoing awareness campaigns, and socio-economic empowerment initiatives, particularly targeting adolescent girls and young women. National scaling of these initiatives is essential to overcoming identified barriers and reducing HIV transmission among this vulnerable population. Additionally, economic empowerment especially among adolescent girls and young women in rural areas has enormous potential to address the spread of HIV in this sub-population.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".