Can a pill prevent<scp>HIV</scp>? Negotiating the biomedicalisation of<scp>HIV</scp>prevention
Bibliographic record
Abstract
This article examines how biomedicalisation is encountered, responded to and negotiated within and in relation to new biomedical forms of HIV prevention. We draw on exploratory focus group discussions on pre-exposure prophylaxis (PrEP) and treatment as prevention (TasP) to examine how the processes of biomedicalisation are affected by and affect the diverse experiences of communities who have been epidemiologically framed as 'vulnerable' to HIV and towards whom PrEP and TasP will most likely be targeted. We found that participants were largely critical of the perceived commodification of HIV prevention as seen through PrEP, although this was in tension with the construction of being medical consumers by potential PrEP candidates. We also found how deeply entrenched forms of HIV stigma and homophobia can shape and obfuscate the consumption and management of HIV-related knowledge. Finally, we found that rather than seeing TasP or PrEP as 'liberating' through reduced levels of infectiousness or risk of transmission, social and legal requirements of responsibility in relation to HIV risk reinforced unequal forms of biomedical self-governance. Overall, we found that the stratifying processes of biomedicalisation will have significant implications in how TasP, PrEP and HIV prevention more generally are negotiated.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".