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Record W1970234893 · doi:10.1332/174426409x395402

Increasing capacity for knowledge translation: understanding how some researchers engage policy makers

2009· article· en· W1970234893 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEvidence & Policy · 2009
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaWestern University
FundersCanadian Health Services Research Foundation
KeywordsGovernment (linguistics)Qualitative researchKnowledge translationPublic relationsProcess (computing)Policy makingResearch policyPublic policyPolitical sciencePolicy analysisSociologyBusinessKnowledge managementPublic administrationSocial scienceComputer science

Abstract

fetched live from OpenAlex

The potential for research to influence policy, and for researchers to influence policy actors, is significant. The purpose of this qualitative study was to explore the experiences of health services researchers engaging in (or not able to engage in) policy-relevant research. Semi-structured telephone interviews were completed with 23 experienced researchers. The results paint a complex and dynamic picture of the policy environment and the relationship between government officials and academic researchers. Elements of this complexity included diverse understandings of the nature of policy and how research relates to policy; dealing with multiple stakeholders in the policy-making process; and identifying strategies to manage the different cultures of government and academia.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.944
GPT teacher head0.714
Teacher spread0.231 · 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