Assessing factors associated with HIV testing among adolescents in Malawi
Bibliographic record
Abstract
Despite being at high risk of HIV/AIDS, most young people do not know their HIV status. Using survey data (n = 2428) and applying multilevel models, this paper assesses factors associated with HIV testing among adolescents in Northern Malawi. The results show that among both boys (OR = 0.39) and girls (OR = 0.47), orphan status is associated with low likelihood of HIV testing. Correct knowledge about HIV/AIDS (OR = 2.55) and having secondary education (OR = 3.24) are associated with HIV testing among boys and girls, respectively. At the household level, living in a household whose head has secondary or higher education is positively associated with testing for boys (OR = 2.63), while residing together with biological siblings predicts higher odds of testing (OR = 2.67) for girls. Notably, orphaned girls' disadvantage regarding HIV testing loses significance when residential arrangement is controlled. At the community level, having HIV testing facility (OR = 2.70) or post-test club (OR = 1.40) is positively associated with HIV testing for boys, while girls from areas where religious leaders hold judgmental views about HIV/AIDS are less likely (OR = 0.45) to test. These findings suggest that efforts to scale up HIV testing among youth could benefit greatly from an understanding of how individual and community factors operate to influence adolescents to know their sero-status.
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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 | 0.000 | 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".