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Abortion Risk and Decisionmaking among Young People in Urban Cameroon

2002· article· en· W2120522397 on OpenAlexaff
Anne‐Emmanuèle Calvès

Bibliographic record

VenueStudies in Family Planning · 2002
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAbortionMedicineUnsafe abortionLogistic regressionPregnancyDemographyParity (physics)Family planningReproductive healthOddsYoung adultObstetricsPopulationFamily medicineGynecologyEnvironmental healthGerontologyResearch methodology

Abstract

fetched live from OpenAlex

This study examines the use of induced abortion among Cameroonian adolescents and young women, based on biographical data collected in 1997 among young women and men residing in Yaoundé. Results suggest a high prevalence of abortion during adolescence and early adulthood (35 percent of all pregnancies reported). Although most clandestine abortions were performed by a physician or a nurse, the prevalence of abortion performed by nonmedical personnel or using unsafe methods is still high, and postabortion health complications are reported for about one-fourth of all abortions. Logistic regression models are used to examine the effect of women's, partners', and relationships' characteristics at the time of pregnancy on the odds that a pregnancy will be terminated through abortion. The analysis shows a significant effect of school enrollment, parity, and stability and social acknowledgment of the sexual relationship on the risk of having an abortion. Young men's involvement in decisions and their motivations concerning abortion are also examined.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.351
Teacher spread0.284 · 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 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

Citations52
Published2002
Admission routes1
Has abstractyes

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