Sexual and reproductive health service utilization among adolescent girls in Kenya: A cross-sectional analysis
Bibliographic record
Abstract
We examined the association between adolescents' sexual and reproductive health (SRH) service utilization in the past 12 months and structural, health facility, community, interpersonal, and individual level factors in Kenya. This cross-sectional analysis used baseline data collected in Homa Bay and Narok counties as part of the In Their Hands intervention evaluation from September to October 2018. In total, 1840 adolescent girls aged 15 to 19 years were recruited to complete a baseline survey. We used unadjusted and adjusted logistic regression to model factors associated with SRH utilization across the social-ecological framework levels. Overall, 36% of participants reported visiting a health facility for SRH services in the past 12 months. At the structural level being out-of-school (AOR: 2.12 95% CI: 1.60-2.82) and not needing to get permission to go (AOR: 1.37 95%CI: 1.04-1.82) were associated with SRH service utilization. At the interpersonal level, participants who reported being able to ask adults for help when they needed it were more likely to report using SRH services in the past 12 months (AOR: 1.98, 95% CI: 1.09-3.78). At the individual level, having knowledge about where to obtain family planning (AOR = 2.48 95% CI: 1.74-3.57) and receiving information on SRH services in the past year (AOR: 1.44 95% CI:1.15-1.80) were associated with SRH service utilization. Our findings demonstrate the need for interventions, policies, and practices to be implemented across structural, health facility, community, interpersonal, and individual levels to comprehensively support adolescent girls to access and use SRH 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".