Self-reported sexually transmitted infections among adolescent girls and young women in Mali: analysis of prevalence and predictors
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence and predictors of self-reported sexually transmitted infections (SR-STIs) among adolescent girls and young women in Mali. DESIGN: We performed a cross-sectional analysis of data from the Demographic and Health Survey of Mali, which was conducted in 2018. A weighted sample of 2105 adolescent girls and young women aged 15-24 was included. Percentages were used to summarise the results of the prevalence of SR-STIs. We used a multilevel binary logistic regression analysis to examine the predictors of SR-STIs. The results were presented using an adjusted odds ratio (aOR) with 95% confidence interval (CI). Statistical significance was set at p<0.05. SETTING: Mali. PARTICIPANTS: Adolescent girls (15-19 years) and young women (20-24 years). OUTCOME MEASURE: SR-STIs. RESULTS: The prevalence of SR-STIs among the adolescent girls and young women was 14.1% (95% CI=12.3 to 16.2). Adolescent girls and young women who had ever tested for HIV, those with one parity, those with multiparity, those with two or more sexual partners, those residing in urban areas, and those exposed to mass media were more likely to self-report STIs. However, those residing in Sikasso and Kidal regions were less likely to report STIs. CONCLUSION: Our study has shown that SR-STIs are prevalent among adolescent girls and young women in Mali. Health authorities in Mali and other stakeholders should formulate and implement policies and programmes that increase health education among adolescent girls and young women and encourage free and easy access to STI prevention and treatment services.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".