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Record W3039777865 · doi:10.1371/journal.pone.0235400

Facilitating stakeholder engagement in early stage translational research

2020· article· en· W3039777865 on OpenAlexfundno aff
Amy LeClair, Virginia Kotzias, Jonathan A. Garlick, Allison Cole, Simona C. Kwon, Alexandra F. Lightfoot, Thomas W. Concannon

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesTufts University School of MedicineUniversity of North Carolina at Chapel HillYork UniversityGeorgia Clinical and Translational Science AllianceUniversity of WashingtonNew York University
KeywordsTranslational researchTranslational scienceStakeholderStakeholder engagementIdentification (biology)Focus groupWork (physics)Engineering ethicsKnowledge managementMedicinePublic relationsPolitical scienceSociologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

INTRODUCTION: Stakeholder engagement can play an important role in increasing public trust and the understanding of scientific research and its impact. Frameworks for stakeholder identification exist, but these frameworks may not apply well to basic science and early stage translational research. METHODS: Four Clinical and Translational Science Award (CTSA) hubs led six focus groups and two semi-structured interviews using a semi-structured discussion guide to learn from basic science researchers about stakeholder engagement in their work. The 24 participants represented fourteen clinical and academic disciplines. RESULTS: Early stage translational researchers reported engagement with a broad array of stakeholders. Those whose research has a clinical focus reported working with a more diverse range of stakeholders than those whose work did not. Common barriers to stakeholder engagement were grouped into three major themes: a poor definition of concepts, absence of guidance, and limited resources. DISCUSSION: The National Center for Advancing Translational Sciences (NCATS), the consortium of CTSAs, and the individual CTSA "hubs" are three actors that can help early stage translational researchers develop shared terms of reference, build the necessary skills, and assemble the appropriate resources for engaging stakeholders in Clinical and Translational Research. Getting this right will involve a coordinated push by all three entities.

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 armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.963
GPT teacher head0.682
Teacher spread0.282 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations25
Published2020
Admission routes1
Has abstractyes

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