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Record W2604914456 · doi:10.1080/13691058.2017.1286690

Barriers to modern contraceptive use in rural areas in DRC

2017· article· en· W2604914456 on OpenAlexaboutno aff
Mbadu Muanda, Gahungu Parfait Ndongo, Lauren J. Messina, Jane T. Bertrand

Bibliographic record

VenueCulture Health & Sexuality · 2017
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
FundersDepartment for International DevelopmentDepartment for International Development, UK GovernmentGovernment of the United Kingdom
KeywordsFamily planningFocus groupQuarter (Canadian coin)Rural areaSociocultural evolutionComprehensionMedicineDeveloping countryPopulationPsychologyEconomic growthPolitical scienceGeographyEnvironmental healthBusinessResearch methodologyMarketing

Abstract

fetched live from OpenAlex

Recent research in the Democratic Republic of Congo (DRC) has shown that over a quarter of women have an unmet need for family planning and that modern contraceptive use is three times higher among urban than rural women. This study focuses on the reasons behind the choices of married men and women to use contraception or not. What are the barriers that have led to low levels of modern contraceptive use among women and men in DRC rural areas? The research team conducted 24 focus groups among women (non-users of any method, users of traditional methods and users of modern methods) and husbands (of non-users or users of traditional methods) in six health zones of three geographically dispersed provinces. The key barriers that emerged were poor spousal communication, sociocultural norms (especially the husband's role as primary decision-maker and the desire for a large family), fear of side-effects and a lack of knowledge. Despite these barriers, many women in the study indicated that they were open to adopting a modern family planning method in the future. These findings imply that programming must address mutual comprehension and decision-making among rural men and women alike in order to trigger positive changes in behaviour and perceptions relating to contraceptive use.

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)0.001
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 score0.010
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.378
Teacher spread0.337 · 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

Citations97
Published2017
Admission routes1
Has abstractyes

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