Risk Factors for Pregnancies Among Females Age 15 to 19 in Rwanda: A Secondary Data Analysis of the 2014/2015 Rwanda Demographic and Health Survey (RDHS)
Bibliographic record
Abstract
Teenage pregnancy is a significant public health problem in developing countries. Due to biological and social factors, teenagers have more negative health outcomes when pregnant. Pregnancy also causes teenagers to drop-out from school, affecting their job opportunities and long-term financial security. As the risk factors for teenage pregnancies are complex and context-specific, effective strategies to reduce teenage pregnancies must be informed by evidence.This study analyzed the data collected by the 2014-2015 Rwanda Demographic and Health Survey in Rwanda. The risk factors associated with 2768 females aged between 15-19 years in the dataset were identified using logistic regression.The overall teenage pregnancy rate was 7.3%. Teens were more likely to have a teenage pregnancy if they were 17 years old (OR=7.04, 95%CI: 2.67 - 18.58, p<0.001), 18 years old (OR=3.78, 95%CI: 1.36 - 10.47, p=0.011), and 19 years old (OR= 3.85, 95%CI: 1.34 - 11.01, p=0.012) compared to teens under 16 years old. Those with secondary or higher education (OR=0.36, 95%CI 0.22 - 0.61, p=<0.001) were less likely to have a teenage pregnancy compared to those with primary school only. Teens had higher odds to have teenage pregnancy if they were married/in union (OR= 45.9, 95%CI: 21.34 - 98.73, P<0.001), and interestingly, if they were using contraceptive methods (OR= 68.9, 95%CI: 29.49 - 160.80, P<0.001).Policy makers should consider programs keeping girls in schools and ensuring that teenagers have access to reproductive health information and reliable contraceptive methods at an early age. Teenage marriage should be discouraged.
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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 | 0.001 |
| 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 | 0.001 |
| 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".