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Record W3107140086 · doi:10.1080/13625187.2020.1850675

Factors associated with knowledge and use of the emergency contraceptive pill among ever-married women of reproductive age in Bangladesh: findings from a nationwide cross-sectional survey

2020· article· en· W3107140086 on OpenAlexaff
Md. Sabbir Ahmed, Fakir Md Yunus

Bibliographic record

VenueThe European Journal of Contraception & Reproductive Health Care · 2020
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicinePillSpouseResidenceDemographyCross-sectional studyMarital statusReproductive healthFamily planningPopulationEmergency contraceptionRural areaEnvironmental healthResearch methodologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: The study aimed to assess the prevalence and factors associated with knowledge and use of the emergency contraceptive pill (ECP) among ever-married women in Bangladesh. METHODS: The study was based on a secondary dataset of the 2014 Bangladesh Demographic and Health Survey. Complete (weighted) data of 17,592 women aged 15-49 years were analysed. RESULTS: The prevalence of having knowledge and use of the ECP among ever-married women in Bangladesh was 13.6% and 1.8%, respectively. Administrative region and type of residence (urban or rural), household wealth index, educational level (of both the woman and her spouse), spouse's occupation, number of living children, weight, current use of contraception and a history of pregnancy termination were positively associated with knowledge and use of the ECP. CONCLUSION: A large proportion of Bangladeshi women of reproductive age had a lack of knowledge and use of the ECP. Nationwide reproductive health education programmes may improve the situation.

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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.077
GPT teacher head0.317
Teacher spread0.240 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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