Development of a STandard reporting guideline for Evidence briefs for Policy (STEP): context and study protocol
Bibliographic record
Abstract
BACKGROUND: Evidence briefs for policy (EBP) draw on best-available data and research evidence (e.g., systematic reviews) to help clarify policy problems, frame options for addressing them, and identify implementation considerations for policymakers in a given context. An increasing number of governments, non-governmental organizations and research groups have been developing EBP on a wide variety of topics. However, the reporting characteristics of EBP vary across organizations due to a lack of internationally accepted standard reporting guidelines. This project aims to develop a STandard reporting guideline of Evidence briefs for Policy (STEP), which will encompass a reporting checklist and a STEP statement and a user manual. METHODS: We will refer to and adapt the methods recommended by the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) network. The key actions include: (1) developing a protocol; (2) establishing an international multidisciplinary STEP working group (consisting of a Coordination Team and a Delphi Panel); (3) generating an initial draft of the potential items for the STEP reporting checklist through a comprehensive review of EBP-related literature and documents; (4) conducting a modified Delphi process to select and refine the reporting checklist; (5) using the STEP to evaluate published policy briefs in different countries; (6) finalizing the checklist; (7) developing the STEP statement and the user manual (8) translating the STEP into different languages; and (9) testing the reliability through real world use. DISCUSSION: Our protocol describes the development process for STEP. It will directly address what and how information should be reported in EBP and contribute to improving their quality. The decision-makers, researchers, journal editors, evaluators, and other stakeholders who support evidence-informed policymaking through the use of mechanisms like EBP will benefit from the STEP. Registration We registered the protocol on the EQUATOR network. ( https://www.equator-network.org/library/reporting-guidelines-under-development/#84 ).
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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 | Metaresearch Domain: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Reporting · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.119 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".