Determinants of HIV Testing Uptake among Undergraduate Students Aged 17-26 Years at the University of Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: The human immunodeficiency virus infection among young people remains a public health concern around the global. To reduce HIV incidences among the young people. To reduce the HIV incidences among the group of young people, the identification of the determinants that influence the uptake of HIV testing especially students are mandatory. Hence help to prevent and control the epidemic. OBJECTIVE: The aim of this study was to identify the determinants that associated with the uptake of HIV testing among undergraduate students at the college of Health Sciences. METHODS: Analytical cross-sectional study was conducted at the University of Nairobi among undergraduate students aged 17- 26 years. Stratified proportionate sampling technique was used to select participants for each school within a college. Data was collected using a closed ended questionnaire and STATA version 11.2 developed by Stata Corp was used to analyse data in which multivariable logistic regression analysis were performed. Confidence interval and level of significance were set at 5% and 20% respectively. RESULTS: Determinants associated with the HIV testing uptake among the young students were as follows; privacy of the location of 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: The uptake of HIV testing was increased by, privacy of Testing location, sexually active and peer influence.
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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".