Relevance and feasibility of principles for health and environmental risk decision-making
Bibliographic record
Abstract
Globally, national regulatory authorities are both responsible and accountable for health and environmental decisions related to diverse products and risk decision contexts. These authorities provided regulatory oversight and expedited market authorizations of vaccines and other therapeutic products during the COVID-19 pandemic. Regulatory decisions regarding such products and situations depend upon well-established risk assessment and management steps. The underlying processes supporting such decisions were outlined in frameworks describing the complex interactions between factors including risk assessment and management steps as well as principles which help guide risk decision-making. In 2022, experts in risk science proposed a set of 10 guiding principles, further examining the intersection and utility of these principles using 10 diverse risk contexts, and inviting a broader discourse on the application of these principles in risk decision-making. To add to this information, Canadian regulatory practitioners responsible for evaluating health and environmental risks and establishing policies convened at a Health Canada workshop on Principles for Risk Decision-Making. This review reports the results derived from this interactive engagement and provides a first pragmatic analysis of the relevance, importance, and feasibility of such principles for health and environmental risk decision-making within the Canadian regulatory context.
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 | 0.107 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".