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Record W1972540199 · doi:10.4236/health.2014.615235

Profiles of HIV-Affected Households in Ghana

2014· article· en· W1972540199 on OpenAlexaff
Amos Laar, Daniel Yaw Fiaveh, Matilda E. Laar, Sandra Boatemaa, James Abugri, Angela El-Adas, Richard Amenyah, Kyeremeh Atuahene, Andrew A. Adjei, Isabella A. Quakyi

Bibliographic record

VenueHealth · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsMcGill University
Fundersnot available
KeywordsLivelihoodHuman immunodeficiency virus (HIV)Asset (computer security)SocioeconomicsFood insecurityGeographyEnvironmental healthDemographyEconomicsMedicineFood securityAgricultureSociology

Abstract

fetched live from OpenAlex

Background: To contribute to a fuller appreciation of Ghana’s HIV epidemic, this paper presents various profiles of the Ghanaian HIV-affected household. To comprehensively tackle the HIV epidemic in Ghana, the profiles would provide stakeholders with ready information for policy formulation. Methods: We used data from a nationally representative survey that measured livelihood activities, household asset wealth, household composition, health, and nutrition variables of 1745 HIV-affected households. From these emerged various profiles. Results: About 50% of the households are headed by females. Households headed by men have an average size of three members, compared to two for female-headed households. There are far more AIDS widows than widowers. The annual death rate among the surveyed households was about 1000 per 100,000-households. Relatively more deaths occurred in male-headed households. Two-thirds of the households were asset poor. Various coping strategies were instituted by the households in reaction to threat of food insecurity. The national prevalence of chronic energy deficiency is 16%. Conclusions: Our data show that age of household head, hosting of a chronically ill member, and average size of household differed by sex of household head. The annual death rate of 1000 per 100,000 households is very high.

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 score0.283
Threshold uncertainty score0.993

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 head0.021
GPT teacher head0.309
Teacher spread0.288 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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