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Record W2012901427 · doi:10.1515/ijamh-2013-0024

Leveraging microfinance to impact HIV and financial behaviors among adolescents and their mothers in West Bengal: a cluster randomized trial

2013· article· en· W2012901427 on OpenAlexaff
Freya Spielberg, Benjamin T. Crookston, Sheila Chanani, Jaewhan Kim, Sean Kline, Bobbi Gray

Bibliographic record

VenueInternational Journal of Adolescent Medicine and Health · 2013
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsReach Technologies (Canada)
Fundersnot available
KeywordsCondomBehavior change communicationMicrofinanceRandomized controlled trialCluster (spacecraft)Human immunodeficiency virus (HIV)MedicineDemographyWest bengalCluster randomised controlled trialEnvironmental healthFamily medicinePsychologySocioeconomicsPopulationEconomic growthSurgeryEconomicsSociology

Abstract

fetched live from OpenAlex

Microfinance can be used to reach women and adolescent girls with HIV prevention education. We report findings from a cluster-randomized control trial among 55 villages in West Bengal to determine the impact of non-formal education on knowledge, attitudes and behaviors for HIV prevention and savings. Multilevel regression models were used to evaluate differences between groups for key outcomes while adjusting for cluster correlation and differences in baseline characteristics. Women and girls who received HIV education showed significant gains in HIV knowledge, awareness that condoms can prevent HIV, self-efficacy for HIV prevention, and confirmed use of clean needles, as compared to the control group. Condom use was rare and did not improve for women. While HIV testing was uncommon, knowledge of HIV-testing resources significantly increased among girls, and trended in the positive direction among women in intervention groups. Conversely, the savings education showed no impact on financial knowledge or behavior change.

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)0.000
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 score0.170
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.031
GPT teacher head0.378
Teacher spread0.347 · 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

Citations20
Published2013
Admission routes1
Has abstractyes

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