The Effect of Human Immunodeficiency Virus Prevention and Reproductive Health Text Messages on Human Immunodeficiency Virus Testing Among Young Women in Rural Kenya
Bibliographic record
Abstract
BACKGROUND: More than half of human immunodeficiency virus (HIV)-infected individuals in Kenya are unaware of their status, and young women carry a disproportionate burden of incident HIV infections. We sought to determine the effect of an SMS intervention on uptake of HIV testing among female Kenyan college students. METHODS: We conducted a quasi-experimental study to increase HIV testing among women 18 to 24 years old. Four midlevel training colleges in Central Kenya were allocated to have their study participants receive either weekly SMS on HIV and reproductive health topics or no SMS. Monthly 9-question SMS surveys were sent to all participants for 6 months to collect data on HIV testing, sexual behavior, and HIV risk perception. We used multivariate Cox proportional hazards regression to detect differences in the time to the first HIV test reported by women during the study period. RESULTS: We enrolled 600 women between September 2013 and March 2014 of whom 300 received weekly SMS and monthly surveys and 300 received only monthly surveys. On average, women were 21 years of age (interquartile range, 20-22), 71.50% had ever had sex and 72.62% had never tested for HIV. A total of 356 women reported testing for HIV within the 6 months of follow-up: 67% from the intervention arm and 51% from the control arm (hazard ratio, 1.57; 95% confidence interval, 1.28-1.92). CONCLUSIONS: Use of weekly text messages about HIV prevention and reproductive health significantly increased rates of HIV testing among young Kenyan women and would be feasible to implement widely among school populations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".