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Record W4206101065 · doi:10.1055/s-0042-1742347

Dismantling Inequities in Adolescent and Young Adult Health through a Sexual and Reproductive Health Justice Approach

2022· review· en· W4206101065 on OpenAlexaff
Romina Barral, Michele A. Kelley, Megan Harrison, María Verónica Svetaz, Yvette Efevbera, Swati Bhave, Nuray Kanbur

Bibliographic record

VenueSeminars in Reproductive Medicine · 2022
Typereview
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsReproductive healthSexual and reproductive health and rightsHealth equityEquity (law)PopulationHealth careEconomic JusticeMedicineReproductive rightsEnvironmental healthNursingPolitical sciencePublic health

Abstract

fetched live from OpenAlex

This article provides an overview of the social determinants of adolescents and young adults' (AYAs') sexual and reproductive health (SRH), from a global health perspective. The status of AYAs' SRH constitutes leading health indicators across nations and globally, and reveals the well-being of this population. Throughout the article, AYAs' SRH is approached from a health equity perspective, which includes SRH health rights and reproductive justice. Using this health equity lens, salient topics are presented: sexual abuse/assault among AYAs; immigrant and refugee populations; child, early, and forced marriage; human trafficking; and female genital mutilation. The article also discusses access to SRH services and comprehensive education. Practical implications and resources are offered for healthcare providers for their daily encounters with AYAs, as well as for community, institutional level, and advocacy action. Healthcare providers are well positioned to advance AYAs SRH through mitigating inequities and in so doing, they are assuring the health of the population and future generations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.262
GPT teacher head0.498
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2022
Admission routes1
Has abstractyes

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