Bibliographic record
Abstract
We thank Groves et al. for their constructive, critical comments. Our paper acknowledges that compulsory HIV testing regimes might deter pregnant women from seeking antenatal care. At this point in time, we do not know whether this actually would be the case, or, if it did act as a deterrent, how many women might be deterred from seeking antenatal care (this kind of cost—as determined by the relevant regulatory authorities—would have to be balanced against lives saved). Our article proposed a pilot study designed to investigate this question, among others. All other things being equal, voluntary counseling and testing is preferable to coercive measures. As our review of the literature indicates, the problem is that, as yet, this has not translated into a particularly efficient means of reducing mother-to-child transmission prevention in high-HIV prevalence areas. We propose to test an alternative to this approach. Considering the continuing high numbers of infected newborns, it is worth investigating whether a coercive approach would yield better public health outcomes. It might not, but by not investigating whether such a strategy would be less suboptimal than the status quo, we are doing a disservice to those at risk of infection. The same holds true for the third concern raised by Groves et al. regarding the effect that a compulsory program could have on increase in risky behavior after pregnancy. They may or may not be right. A considered public health approach to this matter must rest on data as opposed to speculation about what may or may not happen. Hence our argument in favor of a pilot program designed to test what the impact of such a policy change would be. We are a bioethicist and a lawyer, respectively. Our objective was not to discuss the mechanics of such a change of policy. We tried to deal with counterarguments typically deployed to criticize the approach promoted by us. We note that Groves et al. do consider the ethicolegal analysis provided in our article to be “plausible.”
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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 | 0.021 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.057 | 0.027 |
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".