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Record W2150946974 · doi:10.1177/1049732314549813

Diagnostic Technologies in Practice

2014· article· en· W2150946974 on OpenAlexafffund
Daniel Grace, Malcolm Steinberg, Michael Kwag, Sarah Chown, G Doupe, Terry Trussler, Michael L. Rekart, Mark Gilbert

Bibliographic record

VenueQualitative Health Research · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsCommunity Based Research CentreBC Centre for Disease ControlSimon Fraser University
FundersCanadian Institutes of Health Research
KeywordsThematic analysisHuman immunodeficiency virus (HIV)MedicinePublic healthQualitative researchHealth technologyFamily medicinePsychologyNursingHealth careSociologyPolitical science

Abstract

fetched live from OpenAlex

Diagnosing HIV-positive gay men through enhanced testing technologies that detect acute HIV infection (AHI) or recent HIV infection provides opportunities for individual and population health benefits. We recruited 25 men in British Columbia who received an acute (n = 13) or recent (n = 12) HIV diagnosis to engage in a longitudinal multiple-methods study over one year or longer. Our thematic analysis of baseline qualitative interviews revealed insights within men's accounts of technologically mediated processes of HIV discovery and diagnosis. Our analysis illuminated the dialectic of new HIV technologies in practice by considering the relationship between advances in diagnostics (e.g., nucleic acid amplification tests) and the users of these medical technologies in clinical settings (e.g., clients and practitioners). Technological innovations and testing protocols have shifted experiences of learning of one's HIV-positive status; these innovations have created new diagnostic categories that require successful interpretation and translation to be rendered meaningful, to alleviate uncertainty, and to support public health objectives.

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)0.020
metaresearch head score (Gemma)0.176
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.176
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.353
GPT teacher head0.651
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.

Study designNot applicable
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

Citations17
Published2014
Admission routes2
Has abstractyes

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