Trends and factors associated with recent HIV testing among women in Haiti: a cross-sectional study using data from nationally representative surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Introduction In the Latin America and Caribbean region, Haiti is one of the countries with the highest rates of HIV. Therefore, this study examined the factors associated with HIV testing among women in Haiti and trends in HIV testing in 2006, 2012, and 2016/17. Methods Data from the last three Haitian Demographic and Health Surveys (2006, 2012, and 2016/17) were used. The analysis was restricted to women aged of 15–49 years who made their sexual debut. STATA/SE 16.0 was employed to analyze the data by computing descriptive statistics, Chi‑square, and multilevel regression model to describe the trends and identify factors associated with HIV testing in Haiti. P-value less than 0.05 was taken as a significant association. Results HIV testing prevalence increased more than twofold from 2006 (8.8%) to 2017 (21.3%); however, it decreased by 11.6% between 2012 and 2016/17. Additionally, the results indicated that age, place of residence, region, education level, wealth index, mass media exposure, marital status, health insurance, age at first sex and number of sexual partners were significantly associated with HIV testing. Conclusions To significantly increase HIV testing prevalence among women, the Haitian government must invest much more in their health education while targeting vulnerable groups (youth, women in union, and women with low economic status).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.353 | 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 it