MétaCan
Menu
Back to cohort
Record W1982629482 · doi:10.12927/whp.2006.18134

Limitations to Access and Use of Antiretroviral Therapy (ART) Among HIV Positive Persons in Lagos, Nigeria

2006· article· en· W1982629482 on OpenAlexvenueno aff
A.K. Adeneye, T A Adewole, Asmaa Musa, Obinna Onwujekwe, N N Odunukwe, Idowu Araoyinbo, T A Gbajabiamila, Paschal Mbanefo Ezeobi, E.O. Idigbe

Bibliographic record

VenueWorld health & population · 2006
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsHuman immunodeficiency virus (HIV)Health careDeveloping countryPeer reviewNursingNursing researchAntiretroviral therapyMedicineHealth policyAdministration (probate law)Healthcare policyUnit (ring theory)Political scienceMedical educationInternational healthPublic healthFamily medicineEconomic growthPsychology

Abstract

fetched live from OpenAlex

The study was designed to examine the knowledge and perception of HIV positive persons about the antiretroviral therapy (ART) program and to determine their ability to pay for ART and the treatment of other opportunistic infections in Nigeria. This is aimed at identifying factors that may impede effective delivery and utilization of ART in the country. One hundred and twenty-five HIV positive persons seeking ART at the Nigerian Institute of Medical Research (NIMR) clinic, Lagos, were studied using questionnaires. Respondents' average monthly income was N11,253.00 (US$90.00). Almost 26% (25.6%) were unwilling to seek ART at the nearest hospital because of fear of stigmatization. While 9% wanted the therapy for free, the majority was willing to pay N500.00 (US$4.00) per month. The average affordable price based on the subjects' assessment was N905.00 (US$7.24), while the median was N500.00 (US$4.00) per month. Eighty-eight percent believed ART would prolong their lives. The ART drugs need to be affordable and building on the positive perceptions of ART is imperative.

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)0.000
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 score0.033
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.079
GPT teacher head0.392
Teacher spread0.313 · 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

Citations24
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueWorld health & populationSame topicHIV/AIDS Research and InterventionsFrench-language works237,207