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Record W2505097142 · doi:10.1111/nup.12136

Body–drug assemblages: theorizing the experience of side effects in the context of <scp>HIV</scp> treatment

2016· article· en· W2505097142 on OpenAlexafffund
Marilou Gagnon, Dave Holmes

Bibliographic record

VenueNursing Philosophy · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchPublic Health Agency of Canada
KeywordsContext (archaeology)Human immunodeficiency virus (HIV)DrugPsychologySocial psychologyPsychotherapistDevelopmental psychologyMedicinePsychiatryHistoryVirology

Abstract

fetched live from OpenAlex

Each of the antiretroviral drugs that are currently used to stop the progression of HIV infection causes its own specific side effects. Despite the expansion, multiplication, and simplification of treatment options over the past decade, side effects continue to affect people living with HIV. Yet, we see a clear disconnect between the way side effects are normalized, routinized, and framed in clinical practice and the way they are experienced by people living with HIV. This paper builds on the premise that new approaches are needed to understand side effects in a manner that is more reflective of the subjective accounts of people living with HIV. Drawing on the work of Deleuze and Guattari, it offers an original application of the theory of 'assemblage'. This theory offers a new way of theorizing side effects, and ultimately the relationship between the body and antiretroviral drugs (as technologies). Combining theory with examples derived from empirical data, we examine the multiple ways in which the body connects not only to the drugs but also to people, things, and systems. Our objective is to illustrate how this theory dares us to think differently about side effects and allows us to originally (re)think the experience of taking antiretroviral drugs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.041
GPT teacher head0.339
Teacher spread0.298 · 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 designBench or experimental
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

Citations23
Published2016
Admission routes2
Has abstractyes

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