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Record W4205141020 · doi:10.1111/tran.12527

HIV responsibilisation: Stigma, disclosure, and care in the age of 90‐90‐90

2022· article· en· W4205141020 on OpenAlexaff
Brian King, Andrea Rishworth

Bibliographic record

VenueTransactions of the Institute of British Geographers · 2022
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsGeneral Electric (Canada)University of Toronto
FundersPennsylvania State UniversityNational Science Foundation
KeywordsStigma (botany)ScholarshipDeclarationAntiretroviral therapyHuman immunodeficiency virus (HIV)Health careTheme (computing)SociologyEconomic growthPolitical scienceGender studiesPsychologyMedicineFamily medicineLawPsychiatryViral loadEconomics

Abstract

fetched live from OpenAlex

Abstract The announcement that World AIDS Day would mark its 30th anniversary with the theme “know your status” was the result of significant advancements in the global response to the HIV/AIDS epidemic. In making this declaration, UNAIDS emphasised that knowing one's status is crucial to achieving the 90‐90‐90 targets, namely that by 2020, 90% of all people living with HIV will know their status, receive sustained antiretroviral therapy, and have viral suppression. Far removed from an earlier period when access to antiretroviral therapy was limited or unavailable, the “know your status” campaign represents a more hopeful moment. Yet even with its laudatory goal, the campaign reflects a larger trend in global health emphasising that being responsible for one's own health obligates caring for the health of others. The intention of this paper is to engage with geographic scholarship on care to examine how living with HIV involves both personal responsibility and responsibilisation. Drawing from fieldwork in rural South Africa, the paper outlines the challenges for those living with HIV, particularly when stigma and other needs remain stark. We conclude by identifying points of convergence and divergence between theories of responsibility, responsibilisation, and an ethics of care.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Institute of British GeographersSame topicHIV/AIDS Research and InterventionsFrench-language works237,207