Evidence map›Paper›PMID 39119159›Full record

ArticleJournal of the Medical Library Association : JMLA2024

Mobilizing health equity through Computable Biomedical Knowledge (CBK): a call to action to the library, information sciences, and health informatics communities.

Nancy J Allee, Gerald Perry, Gabriel R Rios, Joshua C Rubin, Vignesh Subbian, Deborah E Swain, Terrie R Wheeler

Abstract read
In one paragraph

Article in Journal of the Medical Library Association : JMLA, 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. Article
  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

7 authors.

Nancy J Alleenallee@umich.edu, Director, Taubman Health Sciences Library & STEM, University of Michigan Library, Joint Faculty, Department of Learning Health Sciences, University of Michigan Medical School, University of Michigan, Ann Arbor, MI.
Gerald Perryjerryperry@arizona.edu, Associate Dean Emeritus, University Libraries, University of Arizona, Tucson, AZ.
Gabriel R Riosgrrios@iu.edu, Library Director, Ruth Lilly Medical Library, Indiana University School of Medicine, Indianapolis, IN.ORCID https://orcid.org/0000-0002-4479-8670
Joshua C Rubinjosh@joshcrubin.com, Program Officer for Learning Health System Initiatives, Department of Learning Health Sciences, University of Michigan Medical School, University of Michigan, Ann Arbor, MI.
Vignesh Subbianvsubbian@arizona.edu, Associate Professor, College of Engineering, University of Arizona, Tucson, AZ.ORCID https://orcid.org/0000-0001-9974-8382
Deborah E Swaindswain@nccu.edu, Professor, School of Library and Information Sciences, North Carolina Central University, Durham, NC.
Terrie R Wheelertew2004@med.cornell.edu, Library Director, Samuel J. Wood Library, Weill Cornell Medicine, New York, NY.ORCID https://orcid.org/0000-0003-2933-3696

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The twin pandemics of COVID-19 and structural racism brought into focus health disparities and disproportionate impacts of disease on communities of color. Health equity has subsequently emerged as a priority. Recognizing that the future of health care will be informed by advanced information technologies including artificial intelligence (AI), machine learning, and algorithmic applications, the authors argue that to advance towards states of improved health equity, health information professionals need to engage in and encourage the conduct of research at the intersections of health equity, health disparities, and computational biomedical knowledge (CBK) applications. Recommendations are provided with a means to engage in this mobilization effort.

Indexed as

COVID-19Health EquityMedical InformaticsArtificial IntelligenceHumansLibraries, MedicalSARS-CoV-2AlgorithmsArtificial IntelligenceComputing MethodologiesHealth EquityHealth InequalitiesHealth Status DisparitiesInformation ScienceLibrary ScienceMachine Learning

Identifiers

PMID39119159
PMCPMC11305479

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.