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Record W3015618134 · doi:10.29063/ajrh2020/v24i1.16

Improving Adolescent Access to Contraception in Sub-Saharan Africa: A Review of the Evidence.

2020· review· en· W3015618134 on OpenAlexaff
Julia Smith

Bibliographic record

VenuePubMed · 2020
Typereview
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReproductive healthFamily planningDeveloping countryFocus groupPopulationIntervention (counseling)MedicineEconomic growthPolitical scienceEnvironmental healthNursingSociologyResearch methodology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.666
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.395
GPT teacher head0.479
Teacher spread0.084 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations43
Published2020
Admission routes1
Has abstractyes

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