Evidence map›Paper›PMID 30370066›Full record

ArticleJournal of clinical and translational science2018

Exploring the potential role of community engagement in evaluating clinical and translational science grant proposals.

Jeffrey W Treem, Margaret Schneider, Robynn L Zender, Dara H Sorkin

Abstract read
In one paragraph

Article in Journal of clinical and translational science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jeffrey W TreemDepartment of Communication Studies, The University of Texas at Austin.
Margaret SchneiderInstitute for Clinical and Translational Science, University of California, Irvine.
Robynn L ZenderInstitute for Clinical and Translational Science, University of California, Irvine.
Dara H SorkinInstitute for Clinical and Translational Science, University of California, Irvine.

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
Institute for Clinical and Translational ScienceUL1TR000153 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M · 2012 to 2015
$12.1M
NCATS NIH HHS UL1 TR000153NCATS NIH HHS UL1 TR001414
6 · The paper itself

Abstract

introductionThis study explored the effects of integrating community members into the evaluation of clinical and translational science grants.

methodsThe University of California, Irvine Institute for Clinical and Translational Sciences (ICTS) engaged 21 community reviewers alongside scientific reviewers in a 2-stage process of evaluating research proposals. In Stage 1 reviewers scored proposals, and during Stage 2 two study sections convened: one a mix of community reviewers and scientific reviewers, and one only engaging scientific reviewers. In total, 4 studies were discussed by both study sections.

resultsComparisons of reviews revealed little difference between ratings of community reviewers and those of scientific reviewers, and that community reviewers largely refrained from critiquing scientific or technical aspects of proposals.

conclusionsThe findings suggest that involving community reviewers early in the grant cycle, and exposing them to the entirety of the review process, can bolster community engagement without compromising the rigor of grant evaluations.

Indexed as

Community engagementevaluationgrantsscientific meritscientific review

Identifiers

PMID30370066
PMCPMC6199548

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.