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Record W1985488226 · doi:10.1186/1472-6874-4-s1-s27

Women and HIV

2004· article· en· W1985488226 on OpenAlexaffabout
Marene Gatali, Chris Archibald

Bibliographic record

VenueBMC Women s Health · 2004
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsHealth Canada
Fundersnot available
KeywordsDemographyMedicineContext (archaeology)Human immunodeficiency virus (HIV)Incidence (geometry)GerontologyRisk factorImmunologyGeography

Abstract

fetched live from OpenAlex

HEALTH ISSUE: The epidemic of human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) in developed countries has changed from the early epidemic that affected primarily men who have sex with men, to one that increasingly affects other groups such as injecting drug users (IDU) and heterosexuals. As a result, the number and percentage of women with HIV and AIDS is increasing. KEY FINDINGS: The number of women in Canada living with HIV, including those with AIDS, has increased over time. An estimated 6,800 women were living with HIV at the end of 1999, an increase of 48.0 % from the 1996 estimate of 4,600. On an annual basis, women account for a growing proportion of positive HIV test reports among adults in Canada. This proportion increased from 10.7% in the period 1985-95 to 25% in 2001. Heterosexual contact is the main risk factor for HIV infection in women, accounting for 63% of newly diagnosed cases of HIV infection in adult Canadian women in 2001; the majority of the remainder is due to IDU. KEY DATA GAPS AND RECOMMENDATIONS: Research is needed to address specific information gaps regarding risk behaviours, testing patterns and HIV incidence and prevalence in women. This research needs to include the broader contextual factors that influence women's lives and their risk of HIV infection. Programmes and prevention efforts must be gender and age-specific and should target not only individual behaviours, but also the social and cultural context in which these behaviours occur.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.054
GPT teacher head0.356
Teacher spread0.302 · 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 designQualitative
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

Citations22
Published2004
Admission routes2
Has abstractyes

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