GRADE guidance 40: The GRADE evidence-to-decision framework for environmental and occupational health
Bibliographic record
Abstract
OBJECTIVE: To provide guidance for the use of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence-to-Decision (EtD) framework for environmental and occupational health (EOH). STUDY DESIGN AND SETTING: We conducted a systematic review and narrative synthesis of published and public EOH decision frameworks, followed by a modified Delphi process leading to development of a draft GRADE EtD framework for EOH. We pilot tested the provisional framework through a virtual workshop series, which further informed guidance for the framework's application. We presented a summary of the results to all attendees of the GRADE Working Group meeting for feedback in July 2022 and November 2022, and for approval in May 2023. RESULTS: Consistent with existing GRADE EtD frameworks, the EtD framework for EOH includes a scoping and contextualization process and twelve assessment criteria. Modifications to the existing EtD frameworks include: consideration of the socio-political context when making judgments about the priority of the problem and feasibility of different alternatives; the addition of timing when making judgments about benefits and harms, the balance of effects, and feasibility; broadening of the equity criterion to include considerations beyond health equity; and more explicit accommodation of variable or conflicting stakeholder views when considering values and acceptability. The new EtD framework is also accompanied by a user guide intended to support its implementation in the EOH context. CONCLUSION: Policymakers, regulators, and other stakeholders may use this GRADE EtD framework to approach decision-making about environmental and occupational exposures and interventions.
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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 | 0.325 | 0.647 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.036 | 0.023 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.032 | 0.024 |
| Research integrity | 0.030 | 0.027 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".