Legal Governance in HTA: Environment, Health and Safety Issues / Ethical, Legal and Social Issues (EHSI/ELSI), the Ongoing Debate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it