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Record W2614570756 · doi:10.1136/medethics-2016-103988

Allocation of antiretroviral drugs to HIV-infected patients in Togo: perspectives of people living with HIV and healthcare providers

2017· article· en· W2614570756 on OpenAlexaff
Lonzozou Kpanake, Paul Clay Sorum, Étienne Mullet

Bibliographic record

VenueJournal of Medical Ethics · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsMedicineGuidelineHuman immunodeficiency virus (HIV)Antiretroviral treatmentHealth careAntiretroviral therapyFamily medicineDeveloping countryViral loadPathologyEconomic growth

Abstract

fetched live from OpenAlex

AIM: To explore the way people living with HIV and healthcare providers in Togo judge the priority of HIV-infected patients regarding the allocation of antiretroviral drugs. METHOD: From June to September 2015, 200 adults living with HIV and 121 healthcare providers living in Togo were recruited for the study. They were presented with stories of a few lines depicting the situation of an HIV-infected patient and were instructed to judge the extent to which the patient should be given priority for antiretroviral drugs. The stories were composed by systematically varying the levels of four factors: (a) the severity of HIV infection, (b) the financial situation of the patient, (c) the patient's family responsibilities and (d) the time elapsed since the first consultation. RESULTS: Five clusters were identified: 65% of the participants expressed the view that patients who are poor and severely sick should be treated as a priority, 13% prioritised treatment of patients who are poor and parents of small children, 12% expressed the view that the poor should be treated as a priority, 4% preferred that the sickest be treated as a priority and 6% wanted all patients to get treatment. CONCLUSIONS: WHO's guideline regarding antiretroviral therapy allocation (the sickest first as the sole criterion) currently in use in many African countries does not reflect the preferences of Togolese people living with HIV. For most HIV-infected patients in Togo, patients who cannot get treatment on their own should be treated as a priority.

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)0.002
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.026
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 integrity0.0000.001
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 head0.026
GPT teacher head0.385
Teacher spread0.359 · 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.

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

Citations4
Published2017
Admission routes1
Has abstractyes

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