How Much Do They Know? An Analysis of the Accuracy of HIV Knowledge among Youth Affected by HIV in South Africa
Bibliographic record
Abstract
HIV/AIDS prevalence rates in South Africa are among the highest in the world. The key to reducing transmission is the dissemination of accurate knowledge. Here, we investigate the accuracy of HIV/AIDS knowledge among youth affected by the disease. Data from the Fourth South African National HIV, Behaviour and Health Survey (2012) are used and a weighted sample of 4 095 447 youth (15-24 years old) who have known or cared for someone with HIV/AIDS are analyzed. Results show that more than one-third (40.37%) of youth in South Africa are affected by the disease. One-quarter of the affected youth have 75% accurate knowledge of the virus, while only 10% have 100% accurate knowledge. Rural place of residence (odds ratio [OR] = 0.61) and looking for work (OR = 0.39) are less likely to have accurate knowledge. Youth without disabilities (OR = 2.46), in cohabiting (OR = 1.69), and in dating (OR = 1.70) relationships are more likely to have accurate knowledge. In conclusion, in order to reduce HIV incidence and combat HIV myths, efforts to improve the accuracy of HIV knowledge among youth affected by the disease are needed. There should be more community-based campaigns to target unemployed youth in the country.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".