Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity

Utilizing language models facilitates identification of similar factors and has potential to aid researchers in constructing CLDs whilst reducing the time required to manually merge them. While models accurately merge synonymous or closely related factors, manual intervention may be required for specific cases.

Authors

Michael Johnstone (ORCID: https://orcid.org/0000-0002-3005-8911)
Kristy A. Bolton (ORCID: https://orcid.org/0000-0001-6721-4503)
Douglas Creighton (ORCID: https://orcid.org/0000-0002-9217-1231)
Joshua Hayward (ORCID: https://orcid.org/0000-0001-8484-9930)
M Cabrera

Institutions

Deakin University (AU)

Publication Details

Journal: BMC Public Health
Published: 2025-03-08
DOI: https://doi.org/10.1186/s12889-025-22142-3
Citations: 6
Primary Topic: Health Policy Implementation Science
Type: article
Field-Weighted Citation Impact: 16.89

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article

Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity

Michael Johnstone, Kristy A. Bolton, Douglas Creighton, Joshua Hayward et al.

Mar 8, 2025

6 citations

BMC Public Health

Health Policy Implementation Science

16.89

article

Integration of large-scale community-developed causal loop diagrams: a Natural Language Processing approach to merging factors based on semantic similarity

Michael Johnstone, Kristy A. Bolton, Douglas Creighton, Joshua Hayward, M Cabrera

March 8, 2025article en

6 citations

Abstract

Utilizing language models facilitates identification of similar factors and has potential to aid researchers in constructing CLDs whilst reducing the time required to manually merge them. While models accurately merge synonymous or closely related factors, manual intervention may be required for specific cases.

BMC Public HealthVol. 25(1)

Deakin University (AU)

Quality Education

Openalex Percentile: Top 1%

Health Policy Implementation Science

16.89

DOI Open Access PDF OpenAlex

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