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Record W2774194462 · doi:10.1136/bmjopen-2017-018572

Encouraging translation and assessing impact of the Centre for Research Excellence in Integrated Quality Improvement: rationale and protocol for a research impact assessment

2017· article· en· W2774194462 on OpenAlexaff
Shanthi Ramanathan, Penny Reeves, Simon Deeming, Ross Bailie, Jodie Bailie, Roxanne Bainbridge, Frances Cunningham, Christopher M. Doran, Karen Bell, Andrew Searles

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsInstitute of Health Economics
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMedicineExcellenceProtocol (science)Quality assessmentImpact assessmentHealth services researchQuality (philosophy)Translational researchQuality managementKnowledge translationMedical educationEngineering ethicsPublic healthAlternative medicineNursingKnowledge managementExternal quality assessmentManagementPublic administrationPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: There is growing recognition among health researchers and funders that the wider benefits of research such as economic, social and health impacts ought to be assessed and valued alongside academic outputs such as peer-reviewed papers. Research translation needs to increase and the pathways to impact ought to be more transparent. These processes are particularly pertinent to the Indigenous health sector given continued concerns that Indigenous communities are over-researched with little corresponding improvement in health outcomes. This paper describes the research protocol of a mixed methods study to apply FAIT (Framework to Assess the Impact from Translational health research) to the Centre for Research Excellence in Integrated Quality Improvement (CRE-IQI). FAIT will be applied to five selected CRE-IQI Flagship projects to encourage research translation and assess the wider impact of that research. METHODS AND ANALYSIS: Phase I will develop a modified programme logic model for each Flagship project including identifying process, output and impact metrics so progress can be monitored. A scoping review will inform potential benefits. In phase II, programme logic models will be updated to account for changes in the research pathways over time. Audit and feedback will be used to encourage research translation and collect evidence of achievement of any process, output and interim impacts. In phase III, three proven methodologies for measuring research impact-Payback, economic assessment and narratives-will be applied. Data on the application of FAIT will be collected and analysed to inform and improve FAIT's performance. ETHICS AND DISSEMINATION: This study is funded by a nationally competitive grant (ID 1078927) from the Australian National Health and Medical Research Council. Ethics approval was obtained from the University of Newcastle's Human Research Ethics Committee (ID: H-2017-0026). The results from the study will be presented in several peer-reviewed publications, through conference presentations and via social media.

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)0.575
metaresearch head score (Gemma)0.528
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.425
Threshold uncertainty score0.524

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5750.528
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0060.010
Bibliometrics0.0160.015
Science and technology studies0.0090.018
Scholarly communication0.0160.014
Open science0.0100.016
Research integrity0.0230.020
Insufficient payload (model declined to judge)0.0270.013

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 head0.948
GPT teacher head0.854
Teacher spread0.094 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainEvaluation
GenreProtocol

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

Citations27
Published2017
Admission routes1
Has abstractyes

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