The HIV self-testing debate: where do we stand?
Bibliographic record
Abstract
BACKGROUND: Emphasis on HIV testing as a gateway to prevention, treatment and care has grown tremendously over the past decade. In turn, this emphasis on testing has created a demand for new policies, programs, and technologies that can potentially increase access to and uptake of HIV testing. HIV self-testing (HST) technologies have gained important momentum following the approval of the over-the-counter self-tests in the United States, the UK, and France. While the renewed interest in HST has given rise to a number of high quality reviews of empirical studies conducted on this topic, we have yet to find an article that captures the extent of the debate on HST. MAPPING THE DEBATE: A critical review of the literature on HST was conducted and organized into three categories based on the focus of the article: 1) Empirical research, 2) Arguments, and 3) Context. We focused exclusively on the second category which included ethical analyses, policy analyses, editorials, opinion pieces, commentaries, letters to the editor and so forth. 10 lines of argument on HST were identified in the literature: 1) Individual - Public Health, 2) Strengths - Limits, 3) Benefits - Harms, 4) Screening - Testing, 5) Target - Market, 6) Health Care - Industry, 7) Regulation - Restriction, 8) Resource-Rich Settings - Resource-Limited Settings, 9) Ethical - Unethical, and 10) Exceptionalism - Normalization. Each line of argument is presented and discussed in the paper. CONCLUSION: We conclude by providing examples of critical questions that should be raised in order to take the debate to another level and generate new ways of thinking about HST.
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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 | 0.049 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.028 | 0.038 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".