Co-production and Managing Uncertainty in Health Research Regulation: A Delphi Study
Bibliographic record
Abstract
European and international regulation of human health research is typified by a morass of interconnecting laws, diverse and divergent ethical frameworks, and national and transnational standards. There is also a tendency for legislators to regulate in silos-that is, in discrete fields of scientific activity without due regard to the need to make new knowledge as generalisable as possible. There are myriad challenges for the stakeholders-researchers and regulators alike-who attempt to navigate these landscapes. This Delphi study was undertaken in order to provide the first interdisciplinary and crosscutting analysis of health research regulation, as it is experienced by such stakeholders in the UK context. As well as reinforcing existing understandings of the regulatory environment, Delphi participants called for greater collaboration, and even co-production, of processes involved in health research regulation. On the basis of this research, we offer insights about how health research regulation can become a matter with which a wider range of stakeholders-including researchers, regulators, publics and research sponsors-can engage. The evidence supports the normative claim that health research regulation should continue to move away from strict, prescriptive rules-based approaches, and towards flexible principle-based regimes that allow researchers, regulators and publics to co-produce regulatory systems serving core principles. By unpacking thorny concepts and practices at the heart of health research regulation-including the public interest and public engagement-our results have the potential to situate and breathe life into them. The results also demonstrate that while proportionality is well-recognised as a crucial element of flexible regulatory systems, more must be done to operationalise this as an ethical assessment of the values and risks at stake at multiple junctures in the research trajectory. This is required if we are to move beyond proportionality as a mere risk-management tool. Compliance culture no longer accurately reflects the needs and expectations of researchers or regulators, nor does it necessarily produce the best research. Embracing uncertainty-both as a human practice and a regulatory objective-may represent the brighter future for health research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.243 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".