Is HIV Self-Testing a Potential Answer to the Low Uptake of HIV Testing Services Among Men in Rwanda? Perspectives of Men Attending Tertiary Institutions and Kimisagara Youth Centre in Kigali, Rwanda
Bibliographic record
Abstract
BACKGROUND & OBJECTIVE: Rwanda has generally experienced low uptake of HIV testing services among men. However, the reasons behind this have not been researched. The main aim of this study was to explore whether HIV self-testing (HIVST) would have the capacity to improve uptake of HIV testing services among men in Rwanda. METHODS: We conducted a qualitative study of 22 men attending tertiary institutions and the Kimisagara Youth Centre in Kigali, Rwanda. Data collection was conducted through open interviews. Data analysis was conducted through thematic content analysis. RESULTS: Our findings revealed that most men had poor knowledge of HIVST, but the majority were willing to adopt it. Four main themes emerged during data analysis. Theme one indicated that men experienced a lack of sufficient information on HIVST. From theme two, it was uncovered that some men were indifferent to HIVST. From theme three, it emerged that most men perceived the cost as the main barrier to HIVST; however, if it was offered free of charge, they were willing to adopt it. Finally, theme four revealed that most men willing to adopt HIVST were concerned about the potential social harm and possible adverse events associated with HIVST.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".