Bibliographic record
Abstract
We are pleased that Aral and Blanchard concur that public health needs to move beyond the distinction between biomedical and social dimensions of HIV prevention and resist the increasing biomedicalization of HIV prevention. This move is particularly important in the current context in which HIV prevention is increasingly linked to treatment. Effective HIV prevention, including “treatment as prevention,” requires that people change their social practices and such changes can only be effectively sustained if supported by broad social transformation in environments enabling such changes.1 The biomedicalization of prevention has meant that HIV prevention is increasingly unlikely to be informed by the desires, understandings, and capacities of the communities it is supposed to address. Furthermore, as Aral and Blanchard point out, biomedicalized prevention focuses on identifying universalizable “interventions” (mistakenly thought to be assessable via randomized control trials) rather than developing whole prevention programs (evaluated over time by surveillance and monitoring systems and process evaluations) designed to address the complexity of HIV prevention.2 Although “combination prevention”3 attempts to address complexity, as Aral and Blanchard rightly note, it fails. It fails to recognize that biomedical, behavioral and structural technologies do not always complement one another but may be antagonistic, and that the manner in which HIV prevention technologies are deemed acceptable and taken up depends on the always emergent and fluid local social and cultural contexts in which people have sex and inject drugs. In the absence of an effective vaccine, it is impossible to identify universally effective HIV prevention. Aral and Blanchard call for a Program Science initiative to ensure the optimisation of the choice of the right strategy for the right populations at the appropriate time; the implementation of the right things the right way; and the achievement of appropriate scale and efficiency.4(p157) To this we want to add that choosing and implementing the right strategy for particular populations or subpopulations requires more than program science expertise—no matter how expert. Rather, these decisions must be made by or at least in concert with subpopulations or community members because it is they who inhabit epidemiological and economic and sociopolitical social contexts. It is they who can and do transform them. Social transformation is brought about by the actions of community members, the social practices of community. HIV prevention must recognize and build on what Sen5 refers to as capabilities that enable people to achieve what they desire, rather than imposing a single goal from outside. Effective HIV-prevention programs draw on and build solidarity, common purpose, and collective responsibility. Rather than a program-centered and population-based approach, as called for by Aral and Blanchard, we need an approach that is centered on the social connections within communities in the first instance. Only by recognizing populations as communities with specific desires and capacities is it possible to harness and build community capacity to achieve what they desire—and respond to HIV. As noted at the end of our article, “Social and biomedical scientists can best contribute to understanding prevention in the real world by engaging with efforts to prevent it as they are encountered in life.”(p796)
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.040 | 0.050 |
| Insufficient payload (model declined to judge) | 0.048 | 0.025 |
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".