School attendance and sexual and reproductive health outcomes among adolescent girls in Kenya: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Given the high burden of adverse sexual and reproductive health outcomes (SRH) and low levels of school attendance among adolescent girls in Kenya, this study sought to elucidate the association between school attendance and SRH outcomes among adolescent girls in Homa Bay and Narok counties. METHODS: This study uses baseline quantitative data from the mixed-methods evaluation of the In Their Hands (ITH) program which occurred between September to October 2018 in Homa Bay and Narok counties. In total, 1840 adolescent girls aged 15-19 years participated in the baseline survey, of which 1810 were included in the present analysis. Multivariable logistic regression models were used to assess the association between school attendance (in- versus out-of-school) and ever having sex, condom use during last sex, and ever pregnant, controlling for age, orphan status, income generation, religion, county, relationship status, and correct SRH knowledge. RESULTS: Across the 1810 participants included in our study, 61.3% were in-school and 38.7% were out-of-school. Compared to adolescent girls who were in-school, those out-of-school were more likely (AOR 5.74 95% CI 3.94, 8.46) to report ever having sex, less likely (AOR: 0.21, 95% CI 0.16, 0.31) to have used a condom during their last sexual intercourse, and more likely (AOR: 6.98, 95% CI 5.04, 9.74) to have ever been pregnant. CONCLUSIONS: School attendance plays an integral role in adolescent girls' SRH outcomes, and it is imperative that policy actors coordinate with the government and community to develop and implement initiatives that support adolescent girls' school attendance and education.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".