Evidence map›Paper›PMID 27665067›Full record

ArticleEvaluation and program planning2017

Results from a psychometric assessment of a new tool for measuring evidence-based decision making in public health organizations.

Katherine A Stamatakis, Adriano Akira Ferreira Hino, Peg Allen, Amy McQueen, Rebekah R Jacob, Elizabeth A Baker, Ross C Brownson

Abstract read
In one paragraph

Article in Evaluation and program planning, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Katherine A StamatakisDepartment of Epidemiology, College for Public Health & Social Justice, Saint Louis University, 3545 Lafayette Avenue, St. Louis, MO 63130, United States. Electronic address: kstamata@slu.edu.
Adriano Akira Ferreira HinoDepartment of Physical Education, School of Health and Biosciences, Pontifícia Universidade Católica do Paraná, Curitiba, PR, Brazil. Electronic address: akira.hino@pucpr.br.
Peg AllenPrevention Research Center in St. Louis, Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, United States. Electronic address: pegallen@wustl.edu.
Amy McQueenHealth Communications Research Laboratory, Washington University in St. Louis, 700 Rosedale Avenue, St. Louis, MO 63112, United States. Electronic address: amcqueen@dom.wustl.edu.
Rebekah R JacobPrevention Research Center in St. Louis, Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, United States. Electronic address: rebekahjacob@wustl.edu.
Elizabeth A BakerDepartment of Behavioral Science and Health Education, College for Public Health and Social Justice, Saint Louis University, 3545 Lafayette Ave., St Louis, MO 63130, United States. Electronic address: bakerpa@slu.edu.
Ross C BrownsonPrevention Research Center in St. Louis, Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, MO 63130, United States. Electronic address: rbrownson@brownschool.wustl.edu.

Funding

Washington University Institute of Clinical and Translational SciencesUL1TR000448 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$41.4M
Washington University Institute of Clinical and Translational SciencesKL2TR000450 · NCATS · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2012 to 2016
$8.8M
HPDPRC SIP 13-66U48DP001903 · DP · SAINT LOUIS UNIVERSITY · PI BAKER, ELIZABETH A · 2009 to 2013
$7.3M
Disseminating Evidence-Based Interventions to Control CancerR01CA160327 · NCI · WASHINGTON UNIVERSITY · PI BROWNSON, ROSS C · 2012 to 2017
$2.8M
NCATS NIH HHS KL2 TR000450NCATS NIH HHS UL1 TR000448NCCDPHP CDC HHS U48 DP001903NCI NIH HHS R01 CA160327
6 · The paper itself

Abstract

backgroundIn order to better understand how to improve evidence-based decision making (EBDM) in state health departments, measurement tools are needed to evaluate changes in EBDM. The purpose of this study was to test the psychometric properties of a new measurement tool to assess EBDM in public health practice settings.

methodsA questionnaire was developed, pilot-tested and refined in an iterative process with the input of public health practitioners with the aim of identifying a set of specific measures representing different components of EBDM. Data were collected in a national survey of state health department chronic disease practitioners. The final dataset (n=879) for psychometric testing was comprised of 19 EBDM items that were first examined using exploratory factor analysis, and then confirmatory factor analysis.

resultsThe final model from confirmatory factor analysis includes five latent factors representing components of EBDM: capacity for evaluation, expectations and incentives for EBDM, access to evidence and resources for EBDM, participatory decision making, and leadership support and commitment.

conclusionsThis study addresses the need for empirically tested and theory-aligned measures that may be used to assess the extent to which EBDM is currently implemented, and further, to gauge the success of strategies to improve EBDM, in public health settings. This EBDM measurement tool may help identify needed supports for enhanced capacity and implementation of effective strategies.

Indexed as

Decision MakingAdultChronic DiseaseEvidence-Based PracticeFactor Analysis, StatisticalHumansInformation DisseminationMaleMiddle AgedProgram EvaluationPsychometricsPublic Health AdministrationSurveys and QuestionnairesUnited StatesYoung AdultConfirmatory factor analysisEvidence-based decision makingMeasurementPublic health

Identifiers

PMID27665067
PMCPMC5140729

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.