MétaCan
Menu
Back to cohort
Record W2199732396

Sexual and Reproductive Health Needs of Adolescents in Zimbabwe.

2014· article· en· W2199732396 on OpenAlexaboutno aff
L. Remez, Vanessa Woog, Marvelous Mhloyi

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Reproductive healthDemographyRural areaMedicinePopulationHuman immunodeficiency virus (HIV)FertilityEnvironmental healthGeographyFamily medicineSociology
DOInot available

Abstract

fetched live from OpenAlex

(1) As of 2011, 38% of young Zimbabwean women have had sex by age 18, as have 23% of young men; this difference has widened over time. Females now first have sex nearly two years sooner than males. (2) One-quarter of 15-19-year-old women have started childbearing; one-third of all births to adolescents are unplanned (wanted later or not at all). (3) Favorable trends of rising modern contraceptive use in urban areas were likely interrupted by the worst of the economic crisis in 2008. Use among married adolescents declined in urban areas (from 50% in 2006 to 29% in 2011), even as it rose in rural areas (from 30% to 37%). (4) Patterns in unmet need for contraception followed suit: In urban areas, the proportion of married adolescents who wanted to postpone childbearing but were not using a method rose between 2006 and 2011(from 14% to 28%); among their counterparts in rural areas, unmet need fell from 20% to 15% over this period. (5) Single, sexually active adolescents have by far the greatest unmet need--62% as of 2011, compared with 19% among their married counterparts. (6) Existing policies need clarification to assure that no adolescent is illegally denied services because of age. Youth-friendly sexual and reproductive health programs should be prioritized so today’s HIV-positive adolescents, many of whom have been infected since birth, do not transmit the virus to yet another generation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.378
Teacher spread0.285 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations32
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicAdolescent Sexual and Reproductive HealthFrench-language works237,207