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Record W2588602135 · doi:10.1093/eurpub/ckw167.018

REPOPA indicators for evidence-informed policy making validated by an international Delphi study

2016· article· en· W2588602135 on OpenAlexaff
Valentina Tudisca, Tommaso Castellani, AR Aro, Timo Ståhl, Ien van de Goor, Christina Radl-Karimi, Hilde Spitters, AM Syed, D Rus, Susan Roelofs, Adriana Valente

Bibliographic record

VenueEuropean Journal of Public Health · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDelphi methodDelphiPolicy makingPolitical sciencePsychologyPublic administrationComputer science

Abstract

fetched live from OpenAlex

A total number of 23 measurable REPOPA indicators were organised in four thematic sets - 1) Human resources-Competences & Networking, 2) Documentation-Retrieval/Production, 3) Communication & Participation, 4) Monitoring & Evaluation - and evaluated by means of an international Delphi study. 76 panelists from six European countries (Romania, Italy, Denmark, UK, the Netherlands, Finland) and international organizations, chosen for being researchers or policy makers in public health and across sectors, had to rate relevance and feasibility for each indicator, comment their ratings and propose new indicators by means of two internet-based Delphi rounds. Most indicators were directly validated in the first round, reaching immediately consensus on their high feasibility and relevance; remaining indicators were rated again in the second round, where panelists considered first round scores and comments. Finally 19 out of 23 initial indicators and six out of eight newly suggested indicators were accepted, with a validated list of 25 indicators for EIPM as the final output. Insights emerged as a result of panelists’ ratings and comments, e.g. involvement of researchers was considered essential in all policy phases, while the role of other stakeholders in policy, although considered crucial, raised more discussion, leading to their final exclusion from the policy evaluation phase; allocating budget for EIPM was considered not feasible in most cases, except when devoted to methodologies to engage and consult stakeholders; acquiring evidence from documents was easily welcomed, but citing results from peer reviewed journals and producing evidence on the policy raised more perplexities because of policy makers’ lack of time and familiarity with this kind of literature. Key message: The international Delphi process helped to validate 25 measurable REPOPA indicators aimed at fostering EIPM and produced collective knowledge by means of interaction among policy makers and researchers

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)0.239
metaresearch head score (Gemma)0.228
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2390.228
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0130.009
Science and technology studies0.0030.004
Scholarly communication0.0070.006
Open science0.0030.014
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.002

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 head0.377
GPT teacher head0.564
Teacher spread0.187 · 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.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations1
Published2016
Admission routes1
Has abstractyes

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