The validity of self-reported likelihood of HIV infection among the general population in rural Malawi
Bibliographic record
Abstract
BACKGROUND: Understanding HIV risk perception is important for designing appropriate strategies for HIV/AIDS prevention, because these interventions often rely on behaviour modification. A key component of HIV risk perception is the individual's own assessment of HIV status, and the extent to which this assessment is correct. However, this issue has received limited attention. OBJECTIVES: To examine the validity of self-reported likelihood of current HIV infection among the general population in rural Malawi. METHODS: As part of a panel household survey, data on behaviour and biomarkers were collected for a population-based sample of approximately 3000 respondents in rural Malawi aged > or = 15 years. Information on self-assessed likelihood of currently having HIV was collected by survey interview. Saliva was obtained from all consenting respondents to assess actual HIV status. RESULTS: Of 2299 survey respondents who assessed their likelihood of being infected with HIV at the time of the survey, 71% were accurate. Most incorrect assessments (88%) were due to respondents overestimating (rather than underestimating) their likelihood of being infected with HIV. Women were less likely than men to correctly assess their HIV status. The two most important predictors of false-positive responses were marital status and self-reported health. CONCLUSIONS: Self-reports of HIV infection were generally valid. Most invalid self-reports were due to overestimating the risk of having HIV. The implications of this finding are highlighted, as they pertain to the design of HIV prevention interventions and the expansion of HIV counselling, testing and treatment programmes in developing countries.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".