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Record W2107299871 · doi:10.1186/1478-4505-7-s1-i1

SUPPORT Tools for evidence-informed health Policymaking (STP)

2009· article· en· W2107299871 on OpenAlexaff
John N. Lavis, Andrew D Oxman, Simon Lewin, Atle Fretheim

Bibliographic record

VenueHealth Research Policy and Systems · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMcMaster University
FundersAlliance for Health Policy and Systems ResearchWest China Hospital, Sichuan UniversityDirektoratet for UtviklingssamarbeidUniversity of Cape TownSichuan UniversityHelsedirektoratetUniversity of the Western CapeEuropean CommissionLondon School of Hygiene and Tropical Medicine
KeywordsHealth services researchSet (abstract data type)Framing (construction)Health policyEvidence-based practiceManagement scienceEvidence-based policyPublic relationsEvidence-based medicineHealth administrationComputer sciencePublic healthKnowledge managementMEDLINEPolitical scienceMedicineAlternative medicineNursingEconomicsEngineering

Abstract

fetched live from OpenAlex

This article is the Introduction to a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Knowing how to find and use research evidence can help policymakers and those who support them to do their jobs better and more efficiently. Each article in this series presents a proposed tool that can be used by those involved in finding and using research evidence to support evidence-informed health policymaking. The series addresses four broad areas: 1. Supporting evidence-informed policymaking 2. Identifying needs for research evidence in relation to three steps in policymaking processes, namely problem clarification, options framing, and implementation planning 3. Finding and assessing both systematic reviews and other types of evidence to inform these steps, and 4. Going from research evidence to decisions. Each article begins with between one and three typical scenarios relating to the topic. These scenarios are designed to help readers decide on the level of detail relevant to them when applying the tools described. Most articles in this series are structured using a set of questions that guide readers through the proposed tools and show how to undertake activities to support evidence-informed policymaking efficiently and effectively. These activities include, for example, using research evidence to clarify problems, assessing the applicability of the findings of a systematic review about the effects of options selected to address problems, organising and using policy dialogues to support evidence-informed policymaking, and planning policy monitoring and evaluation. In several articles, the set of questions presented offers more general guidance on how to support evidence-informed policymaking. Additional information resources are listed and described in every article. The evaluation of ways to support evidence-informed health policymaking is a developing field and feedback about how to improve the series is welcome.

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 imitation

Not 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.

metaresearch head score (Codex)0.110
metaresearch head score (Gemma)0.367
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.110
Threshold uncertainty score0.583

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1100.367
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0190.017
Science and technology studies0.0030.004
Scholarly communication0.0220.031
Open science0.0070.024
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0630.044

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.

Opus teacher head0.957
GPT teacher head0.809
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations290
Published2009
Admission routes1
Has abstractyes

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