A Rapid Evidence Support System Assessment (RESSA) of health policymaking in Ireland – A Protocol
Bibliographic record
Abstract
Background: Evidence-informed policymaking promotes the use of the best available evidence in a systematic and transparent manner to guide policy decisions. It aims to ensure that policies are grounded in credible and relevant evidence while also considering factors such as feasibility, sustainability, equity, and stakeholder input. The Global Evidence Commission has emphasised the necessity for stronger national evidence infrastructures and recommended that governments evaluate their evidence-support systems, focusing on the demand for evidence from policymakers, the supply of timely and relevant evidence, and the coordination between the two. To assist countries in reviewing their evidence-support systems, the Global Commission on Evidence to Address Societal Challenges developed the Rapid Evidence Support System Assessment (RESSA). Here, we outline the protocol for a RESSA of health policymaking being conducted in Ireland. Methods: This study will adopt a flexible, mixed-methods design with four key stages: (1) a high-level website review, (2) an in-depth document review, (3) semi-structured interviews with key stakeholders, and (4) seeking feedback. For the document review, the data analysis and synthesis process will follow the READ approach, allowing for a systematic way to organise, interpret, and synthesise the information extracted from the selected documents. Interview data will be analysed using a thematic approach. Findings from both sources will be triangulated to ensure robust conclusions about the strengths and challenges of the evidence-support system for health policymaking. Conclusions: This protocol outlines the methods for assessing Ireland's evidence support system for health policymaking. By documenting our approach in detail, we aim to enhance transparency and replicability, providing a foundation for easier comparison and contrast with similar assessments conducted by other groups. While this study focuses on health, the methodology and findings may also inform evidence-support systems in other sectors, such as climate and education.
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
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 | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.118 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.001 | 0.005 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".