Risk Factors Associated with HIV Counselling and Testing among Undergraduate Students at the College of Health Sciences at the University of Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: The human immunodeficiency virus infection and acquired immune deficiency syndrome epidemic remains a burden worldwide and young people constitute the majority of the burden. Establishing factors that influence the uptake of HIV counselling and testing among young people is mandatory to reduce HIV incidences, and hence help to prevent and control the epidemic. OBJECTIVE: The aim of this study was to determine factors associated with the uptake of HIV counselling and testing among undergraduate students at the University of Nairobi, College of Health Sciences. METHODS: An analytical cross-sectional study was conducted at the University of Nairobi, College of Health Sciences among undergraduate students. Multi-stage sampling technique was used to select participants and a mobile tablet device-based questionnaire on the Open Data Kit application was used to collect data. Univariable logistic regression was performed by using STATA software version 11.2. RESULTS: Factors that were independently associated with the uptake of HIV counselling and testing among the participants were; privacy of the location of the Voluntary Counselling and Testing center (AOR:8.1; 95%Cl:2.7-24.6; p<0.01), peer influence (AOR:1.6; 95%Cl:1.0-2.4; p = 0.05), duration in the academic programme (AOR:0.77; 95%Cl:0.25-2.28; p = 0.03), and whether the participants were sexually active or not (AOR:2.1; 95%Cl:1.3-3.2; p<0.01). CONCLUSION: Privacy during counselling and peer influence are among the risk factors which need to be addressed to increase the uptake of HIV Counselling and Testing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".