Evidence map›Paper›PMID 39047657›Full record

ArticleEvaluation and program planning2024

Use of research evidence in U.S. federal policymaking: A reflexive report on intra-stage mixed methods.

Brett A Diaz, Jessica Pugel, Aditya Phutane, Liwei Zhang, Lawrie Green, Jayne Hoffmann, Elizabeth C Long, Max Crowley, J Taylor Scott

Abstract read
In one paragraph

Article in Evaluation and program planning, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trial
  2. 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

9 authors.

Brett A DiazCentre for Faculty Development, St. Michael's Hospital & University of Toronto, Toronto, Ontario, Canada; The Wilson Centre for Research in Education, University of Toronto, Toronto, Ontario, Canada. Electronic address: brett.diaz@utoronto.ca.
Jessica PugelEvidence-to-Impact Collaborative, Pennsylvania State University, University Park, PA, USA.
Aditya PhutanePublic Administration and Public Affairs, Virginia Tech, VA, USA.
Liwei ZhangBrown School, Washington University in St. Louis, St. Louis, MO, USA.
Lawrie GreenEvidence-to-Impact Collaborative, Pennsylvania State University, University Park, PA, USA.
Jayne HoffmannCollege of Community & Public Affairs, Binghamton University, NY, USA.
Elizabeth C LongEvidence-to-Impact Collaborative, Pennsylvania State University, University Park, PA, USA.
Max CrowleyEvidence-to-Impact Collaborative, Pennsylvania State University, University Park, PA, USA.
J Taylor ScottEvidence-to-Impact Collaborative, Pennsylvania State University, University Park, PA, USA.

Funding

STRATIFIED CLUSTER RANDOMIZED TRIAL (SCRT) OF A CLINICAL DECISION RULE (CDR) FOR ABUSIVE HEAD TRAUMA (AHT)P50HD089922 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI CHRISTIAN M CONNELL · 2017 to 2026
$16.9M
Building the Science of Evidence-Informed Prevention Policy: A Multi-level Model for Supporting Substance Misuse PreventionR01DA056627 · NIDA · PENNSYLVANIA STATE UNIVERSITY, THE · PI Daniel Max Crowley, Jennifer Taylor Scott · 2023 to 2026
$3.0M
Experimental Study of a Model to Support Research Evidence Use for Protecting ChildrenR01HD116152 · NICHD · PENNSYLVANIA STATE UNIVERSITY, THE · PI Daniel Max Crowley, Jennifer Taylor Scott · 2024 to 2026
$2.1M
NICHD NIH HHS P50 HD089922NICHD NIH HHS R01 HD116152NIDA NIH HHS R01 DA056627
6 · The paper itself

Abstract

The policymaking process is largely opaque, especially regarding the actual writing of the policy. To attempt to better understand this complex process, we utilized mixed methods in our evaluation of an intervention. However, the process of mixing methods can be messy, and thus may require recalibration during the evaluation itself. Yet, in comparison to reporting results, relatively little attention is paid to the effects of mixing methods on the evaluation process. In this article, we take a reflexive approach to reporting a mixed methods evaluation of an intervention on the use of research evidence in U.S. federal policymaking. We focus on the research process in a qualitative coding team, and the effects of mixing methods on that process. Additionally, we report in general terms how to interpret multinomial logistic regressions, an underused analysis type applicable to many evaluations. Thus, this reflexive piece contributes (1) findings from evaluation of the intervention on the policymaking process, (2) an example of mixing methods leading to unexpected findings and future directions, (3) a report about the evaluation process itself, and (4) a tutorial for those new to multinomial logistic regressions.

Indexed as

Policy MakingFederal GovernmentHumansLogistic ModelsProgram EvaluationResearch DesignUnited StatesLanguage analysisMixed methodsPolicy analysisRegressionUse of Research Evidence

Identifiers

PMID39047657
PMCPMC11907767

What OpenQuestion holds

Textmetadata
LicenceTDM
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.