Diagnostic Technologies in Practice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".