MétaCan
Menu
Back to cohort
Record W2410243126 · doi:10.1093/eurpub/ckw077

Trends and predictors of knowledge about HIV/AIDS and its prevention and transmission methods among women in Tajikistan

2016· article· en· W2410243126 on OpenAlexaff
Hakim Zainiddinov, Nazim Habibov

Bibliographic record

VenueEuropean Journal of Public Health · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTransmission (telecommunications)DisadvantagedHuman immunodeficiency virus (HIV)Logistic regressionMedicineEnvironmental healthDemographyCluster (spacecraft)Family medicineEconomic growthSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Prior research on HIV infections in Tajikistan and other Central Asian countries has focused primarily on injection drug users. Given the recent rise of heterosexual transmission, especially among women, there is a need to assess women's knowledge about HIV/AIDS and its methods of prevention and transmission across two time periods to examine cross-time changes and identify areas that need improvements. METHODS: Logistic regression and simulation of predicted probability analyses were based on data from Tajik women ranging in age from 15 to 49 who participated in the Multiple Indicator Cluster Survey (MICS) study in 2000 and 2005. RESULTS: We found that an over 2-fold increase in general knowledge about HIV/AIDS was accompanied by a substantial decrease in the ability to identify correct methods of prevention and to reject myths regarding its transmission. CONCLUSION: These alarming findings should prompt policy makers and program implementers to shift the focus of programs from raising general awareness to educating women about how HIV/AIDS is transmitted. Furthermore, rigorous efforts should be made to provide the most disadvantaged groups, including women of younger ages, with lower education, and from poor households with accurate information and adequate access to limited resources.

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)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
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 head0.067
GPT teacher head0.393
Teacher spread0.326 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Public HealthSame topicHIV, Drug Use, Sexual RiskFrench-language works237,207