MétaCan
Menu
Back to cohort
Record W2116228771 · doi:10.1080/07399330903018377

Women's Rights and Women's Health During HIV/AIDS Epidemics: The Experience of Women in Sub-Saharan Africa

2009· article· en· W2116228771 on OpenAlexaff
Begna Dugassa

Bibliographic record

VenueHealth Care For Women International · 2009
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsPublic healthEconomic growthPandemicDeveloping countryPoliticsHuman immunodeficiency virus (HIV)Social issuesMedicineGender studiesSociologyPolitical scienceFamily medicineCoronavirus disease 2019 (COVID-19)NursingDiseaseEconomicsLaw

Abstract

fetched live from OpenAlex

Twenty-five years have passed since HIV/AIDS was recognized as a major public health problem. Although billions of dollars are spent in research and development, we still have no medical cure or vaccination. In the early days of the epidemic, public health slogans suggested that HIV/AIDS does not discriminate. Now it is becoming clear that HIV/AIDS spreads most rapidly among poor, marginalized, women, colonized, and disempowered groups of people more than others. The HIV/AIDS epidemic is exacerbated by the social, economic, political, and cultural conditions of societies such as gender, racial, class, and other forms of inequalities. Sub-Saharan African countries are severely hit by HIV/AIDS. For these countries the pandemic of HIV/AIDS demands the need to travel extra miles. My objective in this article is to promote the need to go beyond the biomedical model of "technical fixes" and the traditional public health education tools, and come up with innovative ideas and strategic thinking to contain the epidemic. In this article, I argue that containing the HIV/AIDS epidemic and improving family and community health requires giving appropriate attention to the social illnesses that are responsible for exacerbating biological disorders.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.345
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 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

Citations14
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueHealth Care For Women InternationalSame topicHIV/AIDS Research and InterventionsFrench-language works237,207