Evidence map›Paper›PMID 34557387›Full record

ArticleMethodsX2021

Using cause-effect graphs to elicit expert knowledge for cross-impact balance analysis.

Ivana Stankov, Andres F Useche, Jose D Meisel, Felipe Montes, Lidia Mo Morais, Amelia Al Friche, Brent A Langellier, Peter Hovmand, Olga L Sarmiento, Ross A Hammond and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Research Translation to Promote Urban Health in Latin America: The SALURBAL Experience.Journal of urban health : bulletin of the New York Academy of Medicine · 2024
    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

11 authors.

Ivana StankovUrban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USA.
Andres F UsecheDepartment of Industrial Engineering, Universidad de Los Andes, Bogotà, Colombia.
Jose D MeiselSocial and Health Complexity Center, Universidad de Los Andes, Bogotà, Colombia.
Felipe MontesDepartment of Industrial Engineering, Universidad de Los Andes, Bogotà, Colombia.
Lidia Mo MoraisObservatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil.
Amelia Al FricheObservatory for Urban Health in Belo Horizonte, Belo Horizonte, Brazil.
Brent A LangellierDepartment of Health Management and Policy, Dornsife School of Public Health, Drexel University, 3215 Market St, Philadelphia, PA 19104, USA.
Peter HovmandCenter for Community Health Integration, Case Western Reserve University, Cleveland, OH, USA.
Olga L SarmientoDepartment of Public Health, School of Medicine, Universidad de los Andes, Bogotà, Colombia.
Ross A HammondBrown School at Washington University in St. Louis, One Brookings Drive, St Louis, MO 36130, USA.
Ana V Diez RouxUrban Health Collaborative, Dornsife School of Public Health, Drexel University, 3600 Market St, Philadelphia, PA 19104, USA.

Funding

Washington University Center for Diabetes Translation Research P30DK092950 · NIDDK · WASHINGTON UNIVERSITY · PI Ross C Brownson, Debra Haire-Joshu · 2011 to 2026
$11.7M
NIDDK NIH HHS P30 DK092950Wellcome Trust 205177
6 · The paper itself

Abstract

Cross-impact balance (CIB) analysis leverages expert knowledge pertaining to the nature and strength of relationships between components of a system to identify the most plausible future 'scenarios' of the system. These scenarios, also referred to as 'storylines', provide qualitative insights into how the state of one factor can either promote or restrict the future state of one or multiple other factors in the system. This paper presents a novel, visually oriented questionnaire developed to elicit expert knowledge about the relationships between key factors in a system, for the purpose of CIB analysis. The questionnaire requires experts to make selections from a series of standardized cause-effect graphical profiles that depict a range of linear and non-linear relationships between factor pairs. The questionnaire and the process of translating the graphical selections into data that can be used for CIB analysis is described using an applied example which focuses on urban health in Latin American cities.

Indexed as

Chronic diseaseComplex SystemsDietEpidemiologyFood environmentScenario analysisSystems thinkingTransportation systemUrban Health

Identifiers

PMID34557387
PMCPMC7611690

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.