Key attributes of health and environmental risk decision‐making: A scoping review
Bibliographic record
Abstract
Government agencies, international institutions, and independent experts have published approaches for the assessment and management of health and environmental risks. This includes evidence-based strategies and publications supporting risk decision-making frameworks reflecting contemporary practices, the overarching context, and governance structures for addressing known and emerging risk issues. This scoping review surveys the literature, over the last five decades, to identify key attributes of health and environmental risk decision-making and how these inherent characteristics are related to the overarching regulatory decision-making context. The findings provide insights on how these publications accounted for the circumstances and triggers at that time. This includes incorporating factors reflecting advances in science and technology, a better understanding of underlying values (e.g., societal), and an expansion in the scope and complexity required for conducting different evaluations relevant to health and environmental risks. Consequently, the evolution from linear to more expanded and holistic decision-making frameworks incorporates foundational elements, such as the well-established steps for assessing risks, while adding aspects reflecting transformative changes and paradigm shifts (e.g., the use of non-animal testing strategies for evaluating human safety). Our analysis also resulted in the generation of a consolidated listing of ten attributes: trigger/issue, regulatory context, regulatory factors, core values, risk decision-making principles, cross-cutting attributes, design (scope and steps), structure, decision-making pathway, and evidence-knowledge requirements for risk decision-making. A better understanding of this evolution in risk decision-making and the listing of key attributes will be used in future work aimed at developing considerations for next generation decision-making approaches for health and environmental risks.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".