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Record W1985976025 · doi:10.1155/2011/376432

Prevalence of and Barriers to Dual-Contraceptive Methods Use among Married Men and Women Living with HIV in India

2011· article· en· W1985976025 on OpenAlexaff
Venkatesan Chakrapani, Trace Kershaw, Murali Shunmugam, Peter A. Newman, Deborah H. Cornman, Robert Dubrow

Bibliographic record

VenueInfectious Diseases in Obstetrics and Gynecology · 2011
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsUniversity of Toronto
FundersFogarty International CenterDepartment for International DevelopmentDepartment for International Development, UK GovernmentYale University
KeywordsMedicineFamily planningPsychological interventionHuman immunodeficiency virus (HIV)Family medicineFocus groupPopulationGynecologyGerontologyDemographyEnvironmental healthResearch methodologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the prevalence and correlates of dual-contraceptive methods use (condoms and an effective pregnancy prevention method) and barriers to their use among married persons living with HIV (PLHIV) in India. METHODS: We conducted a quantitative survey (93 men, 97 women), 25 in-depth interviews, seven focus groups, and five key informant interviews. RESULTS: Prevalence of dual-contraceptive method use increased from 5% before HIV diagnosis to 23% after diagnosis (P < 0.001). Condoms were the most common contraceptive method, with prevalence increasing from 13% before diagnosis to 92% after diagnosis (P < 0.001). Barriers to using noncondom contraceptives were lack of discussion about noncondom contraceptives by health care providers, lack of acceptability of noncondom contraceptives among PLHIV, and lack of involvement of husbands in family planning counseling. CONCLUSION: There is a need for interventions, including training of health care providers, to increase dual-contraceptive methods use among married PLHIV.

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)0.001
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.285
Teacher spread0.272 · 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

Citations53
Published2011
Admission routes1
Has abstractyes

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