Improving Adolescent Access to Contraception in Sub-Saharan Africa: A Review of the Evidence.
Bibliographic record
Abstract
Global commitments to and support for sexual and reproductive health (SRH) have increased over the past 10 years. Adolescent access to contraception has emerged as a crucial area of focus within this agenda, particularly in sub-Saharan Africa (SSA), where there is the greatest unmet need for contraception. Yet there is little synthesized knowledge around adolescents' use and knowledge of, and access to contraception in SSA. This review summarizes and analyzes literature on the subject in order to determine implications for policy and program development, and to guide future research. The majority of existing research focuses on South Africa, with numerous studies from East Africa also present. Most of this research is qualitative, with few mixed method studies, and only one randomized control trial of an intervention. Findings from multiple countries confirm that adolescents in SSA have a significant unmet need for contraception. Most adolescents get their information about contraception from the media or peers. Persistent myths regarding effectiveness and side effects, as well as cultural and gender norms, impede access to and demand for contraception. Other determinants of access and use include education level and socio-economic status. As a result, intervention evaluations note that cultural barriers and socio-economic conditions limit SRH outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".