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Record W2159474640

Oral contraceptive use.

2000· article· en· W2159474640 on OpenAlexaffabout
Kate Wilkins, Helen Johansen, Beaudet Mp, Neutel Ci

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMedicineLogistic regressionDemographyMedical prescriptionPopulationCross-sectional studyEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: This article profiles Canadian women aged 15 to 49 who use oral contraceptives (OCs), and compares certain of their characteristics with those of non-users. It also examines associations between OC use and selected characteristics, including cardiovascular risk factors. DATA SOURCE: The data are from the cross-sectional household component of Statistics Canada's 1996/97 National Population Health Survey. The analysis is based on a sample of 21,996 women aged 15 to 49, weighted to represent an estimated 7.6 million women. ANALYTICAL TECHNIQUES: Cross-tabulations were used to estimate the percentage of women aged 15 to 49 who use OCs and to compare selected health behaviours of users and non-users. A multiple logistic regression model was used to model relationships between selected characteristics and OC use. MAIN RESULTS: An estimated 1.3 million women aged 15 to 49, or 18%, reported using OCs in 1996/97. OC use was significantly associated with being young, unmarried, sexually active, and having prescription drug insurance and relatively high education. About one-third of OC users also smoked.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.365
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1160.024

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 head0.044
GPT teacher head0.274
Teacher spread0.229 · 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
GenreOther

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

Citations67
Published2000
Admission routes2
Has abstractyes

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