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Record W2136153070 · doi:10.1177/1049732315575315

Acceptable Care? Illness Constructions, Healthworlds, and Accessible Chronic Treatment in South Africa

2015· article· en· W2136153070 on OpenAlexafffund
Jana Fried, Bronwyn Harris, John Eyles, Mosa Moshabela

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchHealth CanadaUniversity of Cape TownPublic Health AgencyUniversity of the Western CapeInternational Development Research CentreInyuvesi Yakwazulu-NataliPublic Health Agency of CanadaMcMaster University
KeywordsHealth careContext (archaeology)Affect (linguistics)MedicineNarrativeNursingPsychologyEconomic growthGeography

Abstract

fetched live from OpenAlex

Achieving equitable access to health care is an important policy goal, with access influenced by affordability, availability, and acceptability of specific services. We explore patient narratives from a 5-year program of research on health care access to examine relationships between social constructions of illness and the acceptability of health services in the context of tuberculosis treatment and antiretroviral therapy in South Africa. Acceptability of services seems particularly important to the meanings patients attach to illness and care, whereas-conversely-these constructions appear to influence what constitutes acceptability and hence affect access to care. We highlight the underestimated role of individually, socially, and politically constructed healthworlds; traditional and biomedical beliefs; and social support networks. Suggested policy implications for improving acceptability and hence overall health care access include abandoning patronizing approaches to care and refocusing from treating "disease" to responding to "illness" by acknowledging and incorporating patients' healthworlds in patient-provider interactions.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.369
GPT teacher head0.563
Teacher spread0.194 · 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 designQualitative
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

Citations19
Published2015
Admission routes2
Has abstractyes

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