Evidence map›Paper›PMID 35175501›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2022

Transparent, Open, and Reproducible Prevention Science.

Sean Grant, Kathleen E Wendt, Bonnie J Leadbeater, Lauren H Supplee, Evan Mayo-Wilson, Frances Gardner, Catherine P Bradshaw

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Toward AI-Assisted Methodological Evaluations of Health Risk Analyses Using Diverse Types of Evidence.Risk analysis : an official publication of the Society for Risk Analysis · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Review
  13. Open Science Standards at Journals that Inform Evidence-Based Policy.Prevention science : the official journal of the Society for Prevention Research · 2023
    Review
  14. Article
  15. Article
  16. Article
  17. Moving Toward Transparent, Open, and Reproducible Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2022
    Article
  18. Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2022
    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

7 authors.

Sean GrantDepartment of Social & Behavioral Sciences, Fairbanks School of Public Health, Indiana University Richard M, 1050 Wishard Blvd, Indianapolis, IN, 46202, USA. spgrant@iu.edu.ORCID 0000-0002-7775-3022
Kathleen E WendtDepartment of Human Development and Family Studies, Colorado State University, Fort Collins, CO, USA.
Bonnie J LeadbeaterDepartment of Psychology, University of Victoria, Victoria, BC, Canada.
Lauren H SuppleeChild Trends, Bethesda, MD, USA.
Evan Mayo-WilsonDepartment of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
Frances GardnerDepartment of Social Policy and Intervention, University of Oxford, Oxford, UK.
Catherine P BradshawSchool of Education & Human Development, University of Virginia, Charlottesville, VA, USA.

Funding

The integrated Translational Health Research Institute of Virginia (iTHRIV): Using Data to Improve HealthUL1TR003015 · NCATS · UNIVERSITY OF VIRGINIA · PI BROWN, DONALD E, JOHNSTON, KAREN C. · 2019 to 2023
$20.3M
NCATS NIH HHS UL1 TR003015
6 · The paper itself

Abstract

The field of prevention science aims to understand societal problems, identify effective interventions, and translate scientific evidence into policy and practice. There is growing interest among prevention scientists in the potential for transparency, openness, and reproducibility to facilitate this mission by providing opportunities to align scientific practice with scientific ideals, accelerate scientific discovery, and broaden access to scientific knowledge. The overarching goal of this manuscript is to serve as a primer introducing and providing an overview of open science for prevention researchers. In this paper, we discuss factors motivating interest in transparency and reproducibility, research practices associated with open science, and stakeholders engaged in and impacted by open science reform efforts. In addition, we discuss how and why different types of prevention research could incorporate open science practices, as well as ways that prevention science tools and methods could be leveraged to advance the wider open science movement. To promote further discussion, we conclude with potential reservations and challenges for the field of prevention science to address as it transitions to greater transparency, openness, and reproducibility. Throughout, we identify activities that aim to strengthen the reliability and efficiency of prevention science, facilitate access to its products and outputs, and promote collaborative and inclusive participation in research activities. By embracing principles of transparency, openness, and reproducibility, prevention science can better achieve its mission to advance evidence-based solutions to promote individual and collective well-being.

Indexed as

Reproducibility of ResultsHumansOpen sciencePreventionReplicationReproducibilityResearch transparency

Identifiers

PMID35175501
PMCPMC9283153

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.