Endorsement of HIV misconceptions over time among females and males in Haiti
Bibliographic record
Abstract
To address high HIV prevalence rates in Haiti, disseminating information about HIV transmission has been emphasized. Yet, after several decades, we do not know how effective HIV information dissemination has been in reducing HIV misconceptions. Using the 2005-06, 2012, and 2016-17 Haiti Demographic and Health Surveys and applying logistic regression, we found nuanced gender dynamics in endorsing HIV misconceptions over time. Among females at the bivariate level, the odds of endorsement of HIV misconceptions in 2012 (OR = 0.87, p < 0.05) and 2016-17 (OR = 0.68, p < 0.001) had declined compared to 2005-06. At the multivariate level, however, we observed that demographic factors suppressed the difference between 2005-06 and 2012, although those in 2016-17 (OR = 0.71, p < 0.001) were still less likely to endorse HIV misconceptions. However, this relationship disappeared once we added behavioral factors (OR = 0.93, p > 0.05). Among males, after controlling for demographic, socioeconomic, and behavioral factors at the multivariate level, those in 2012 (OR = 1.55, p < 0.001) and 2016-17 (OR = 1.24, p < 0.01) were more likely to endorse HIV misconceptions compared to men in 2005-06. We recommend that while improving women’s access to HIV services, it is important to incorporate the HIV needs of males into the National HIV policy priority areas.
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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 | 0.004 |
| 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 | 0.000 | 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".