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Record W4400057527 · doi:10.1007/s10912-024-09862-0

Illness Narratives Without the Illness: Biomedical HIV Prevention Narratives from East Africa

2024· article· en· W4400057527 on OpenAlexfundno aff
Jason Johnson‐Peretz, Fredrick Atwine, Moses R. Kamya, James Ayieko, Maya Petersen, Diane V. Havlir, Carol S. Camlin

Bibliographic record

VenueJournal of Medical Humanities · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Institutes of HealthInternational Development Research Centre
KeywordsNarrativeContext (archaeology)Sociology of health and illnessNarrative inquiryMedicineGender studiesPsychologySociologyHealth careHistoryLiteraturePolitical science

Abstract

fetched live from OpenAlex

Illness narratives invite practitioners to understand how biomedical and traditional health information is incorporated, integrated, or otherwise internalized into a patient's own sense of self and social identity. Such narratives also reveal cultural values, underlying patterns in society, and the overall life context of the narrator. Most illness narratives have been examined from the perspective of European-derived genres and literary theory, even though theorists from other parts of the globe have developed locally relevant literary theories. Further, illness narratives typically examine only the experience of illness through acute or chronic suffering (and potential recovery). The advent of biomedical disease prevention methods like post- and pre-exposure prophylaxis (PEP and PrEP) for HIV, which require daily pill consumption or regular injections, complicates the notion of an illness narrative by including illness prevention in narrative accounts. This paper has two aims. First, we aim to rectify the Eurocentrism of existing illness narrative theory by incorporating insights from African literary theorists; second, we complicate the category by examining prevention narratives as a subset of illness narratives. We do this by investigating several narratives of HIV prevention from informants enrolled in an HIV prevention trial in Kenya and Uganda in 2022.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.332
Teacher spread0.289 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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