Acceptability of HIV Screening in a Sample of International Students in the United States
Bibliographic record
Abstract
Background and Objectives: HIV transmission from persons unaware of their HIV status occurs more commonly than those who are aware of their status. Knowledge of one’s HIV status may encourage preventive behaviors. Anecdotal evidence suggests that many international students may be willing to accept HIV screening, but empirical evidence to support this claim is lacking. We sought to determine the willingness of international students in the United States (US) to accept HIV screening, if offered. Methods: We conducted a cross-sectional study using an online survey of international students at Western Illinois University, USA. The independent variable was the sociodemographic data of our participants; the dependent variable was the acceptance of HIV screening. The covariates were knowledge about HIV and the factors associated with the acceptance of the screening. Descriptive statistics and multivariate analysis were conducted. Results: A total of 185 respondents out of 491 students participated in the online survey. Of these, 107 (57.8%) were males, and 78 (42.8%) were females. Most of the respondents were from Asian countries (64.9%) and African countries (24.9%). The prevalence of acceptance of HIV screening was 74%. Among participants willing to accept screening, if offered, 90% perceived screening would be beneficial to their health. Meanwhile, 83% of those who would refuse the screening were not sexually active. Conclusion and Global Health Implications: Many international students may be interested in getting HIV screening if offered. Awareness of the benefits of HIV screening may influence the decision to screen. Findings may inform further studies that will lead to policy formulations for the health of international students in the US. Key words: • HIV Screening • HIV Acceptability • International Students • College Students Copyright © 2020 Ayosanmi et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unre-stricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.
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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".