Ethical considerations in post-market-approval monitoring and regulation of vaccines
Bibliographic record
Abstract
The objective of this paper is to identify and articulate ethical considerations to help guide decision-making around the regulation and monitoring of vaccines post-licensure. While these considerations are not intended to be an exhaustive account of the ethical concerns, they can facilitate the explicit examination of ethical issues in this context. We identify the protection of public from harm as the primary consideration, and identify others that help in the discharging of this governmental obligation. Others include: transparency, a publicly acceptable risk-benefit profile, public trust, minimization of stigma, and special obligations to vulnerable populations. Regulators and researchers can use these ethical considerations to help enhance their reasoning and to improve the accountability of their decision-making. These considerations can be used to inform rational deliberations about how to balance the obligation to protect the public from harm with other relevant considerations such as the need to be transparent, while taking into account the contextual features of the situation. Further research and debate on the relevance and refinement of these ethical considerations is desirable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.321 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".