Legal Governance in HTA: Environment, Health and Safety Issues / Ethical, Legal and Social Issues (EHSI/ELSI), the Ongoing Debate
Bibliographic record
Abstract
This paper aims to provide a better understanding of the law circumscribing the social role of Health Technology Assessment (HTA) and gain insight into the reasons challenging the inclusion of ethics into HTA. We focused on a debate at the core of the perceived role of regulatory law in health technology development, namely: Environment, Health and Safety Issues (EHSI) vs Ethical, Legal and Social Issues (ELSI) that arose in technology governance. Data collection was based on a literature review and a case study analysis. The former was founded on previous work. Three HTA agencies were selected for the latter using categories ranging from a greater to a lesser level of legal obligatory intensity. Our literature review revealed five different themes relating to the social role of HTA and a distinction between the role/use of “hard law” and “soft law” in regulatory law, thus providing an understanding of how agencies used law for handling ethics in HTA. Both approaches revealed that the debate, first observed in the EHSI/ELSI technology-governance and assessment, is reproduced in HTA. The main trend revealed by the literature review and the case study, is the presence of a pact between science and regulatory law. The social demand for integrating ELSI, and more precisely, ethical evaluation into HTA, is not the main preoccupation of the traditional legal frameworks governing HTA and remains to be considered primarily by alternative, soft law initiatives. The reported difficulties in integrating ethics into HTA demonstrate the need for rethinking legal governance in HTA.
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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 | 0.073 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.087 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".