Advancing population and public health ethics regarding HIV testing: a scoping review
Bibliographic record
Abstract
Recently, scholars have called for more robust population and public health ethical frameworks to inform how the health of populations and individuals ought to be improved through various approaches to HIV testing practices. Our objective is to examine the breadth, range and foci of a variety of ethical issues pertaining to HIV testing approaches within the peer-reviewed literature, and how these issues address population and/or individual interests. We identify potential tensions between individual and collective approaches as well as other concerns, including equity, justice and distribution of health and risk – hallmarks of the emergent field of population and public health ethics. Based on our review, we suggest that additional theoretical work and empirical research are required in order to inform more ethically robust debates related to population HIV testing practices. Specifically problematic were consequentialist arguments that deem testing approaches as either morally permissible or impermissible without sufficient robust empirical and/or theoretical underpinnings and about how a particular approach would unfold among individuals and populations. The current review underscores the need to continue to articulate an evidence- and theory- informed population and public health ethics pertaining to HIV testing.
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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 | 0.046 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".