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Record W4211192955 · doi:10.1002/psp.2552

Aging, (un)certainty and HIV management in South Africa

2022· article· en· W4211192955 on OpenAlexaff
Andrea Rishworth, Brian King

Bibliographic record

VenuePopulation Space and Place · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCertaintyDisadvantageLivelihoodHuman immunodeficiency virus (HIV)Qualitative researchSociologyGerontologyPolitical sciencePsychologyMedicineSocial scienceGeographyEpistemology

Abstract

fetched live from OpenAlex

Abstract Research within geography and cognate disciplines demonstrates how (un)certainty informs relational, emergent and open‐ended processes of healthy aging. Although (un)certainty shapes aging health inequities and possibilities for reconfiguration, research often centres on challenges for aging individuals, eliding more dynamic, complex and contradictory factors shaping the health and wellbeing of aging individuals and societies. This paper uses qualitative research with older women in South Africa to engage contradictions in (un)certainty. We argue that while advances in HIV/AIDS testing and treatment allow individuals to grow older with greater certainty, longer lives managing HIV rework new forms of ill‐health through contested disease etiology, indeterminant multitemporal processes, and dubious livelihood prospects. (Un)certain HIV landscapes can create new forms of disadvantage and subjection, while in other instances encourage opportunities for healthier lives and pragmatic social change. The findings highlight the importance of considering (un)certain aging, health and disease realities and the structures that assuage them.

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 score0.319
Threshold uncertainty score0.411

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 studies0.0010.000
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 head0.025
GPT teacher head0.302
Teacher spread0.277 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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