Evidence map›Paper›PMID 34192573›Full record

ArticleJournal of biomedical informatics2021

Developing a sampling method and preliminary taxonomy for classifying COVID-19 public health guidance for healthcare organizations and the general public.

Peter Taber, Catherine J Staes, Saifon Phengphoo, Elisa Rocha, Adria Lam, Guilherme Del Fiol, Saverio M Maviglia, Roberto A Rocha

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

8 authors.

Peter TaberDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States. Electronic address: peter.taber@hsc.utah.edu.
Catherine J StaesDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; College of Nursing, University of Utah, Salt Lake City, UT, United States.
Saifon PhengphooDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States; College of Nursing, University of Utah, Salt Lake City, UT, United States.
Elisa RochaUniversity of Massachusetts Medical School, Worcester, MA, United States.
Adria LamAlbany Medical College, Albany, NY, United States.
Guilherme Del FiolDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
Saverio M MavigliaBrigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Semedy, Inc, Needham, MA, United States.
Roberto A RochaBrigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Semedy, Inc, Needham, MA, United States.

Funding

UNIVERSITY OF UTAH MEDICAL INFORMATICS TRAININGT15LM007124 · NLM · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Karen Louise Eilbeck · 1997 to 2026
$22.0M
NLM NIH HHS T15 LM007124
6 · The paper itself

Abstract

backgroundDevelopment and dissemination of public health (PH) guidance to healthcare organizations and the general public (e.g., businesses, schools, individuals) during emergencies like the COVID-19 pandemic is vital for policy, clinical, and public decision-making. Yet, the rapidly evolving nature of these events poses significant challenges for guidance development and dissemination strategies predicated on well-understood concepts and clearly defined access and distribution pathways. Taxonomies are an important but underutilized tool for guidance authoring, dissemination and updating in such dynamic scenarios.

objectiveTo design a rapid, semi-automated method for sampling and developing a PH guidance taxonomy using widely available Web crawling tools and streamlined manual content analysis.

methodsIterative samples of guidance documents were taken from four state PH agency websites, the US Center for Disease Control and Prevention, and the World Health Organization. Documents were used to derive and refine a preliminary taxonomy of COVID-19 PH guidance via content analysis.

resultsEight iterations of guidance document sampling and taxonomy revisions were performed, with a final corpus of 226 documents. The preliminary taxonomy contains 110 branches distributed between three major domains: stakeholders (24 branches), settings (25 branches) and topics (61 branches). Thematic saturation measures indicated rapid saturation (≤5% change) for the domains of "stakeholders" and "settings", and "topic"-related branches for clinical decision-making. Branches related to business reopening and economic consequences remained dynamic throughout sampling iterations.

conclusionThe PH guidance taxonomy can support public health agencies by aligning guidance development with curation and indexing strategies; supporting targeted dissemination; increasing the speed of updates; and enhancing public-facing guidance repositories and information retrieval tools. Taxonomies are essential to support knowledge management activities during rapidly evolving scenarios such as disease outbreaks and natural disasters.

Indexed as

COVID-19Public HealthDelivery of Health CareHumansPandemicsSARS-CoV-2Content analysisCOVID-19IndexingKnowledge managementPublic health guidanceTaxonomy

Identifiers

PMID34192573
PMCPMC8236411

What OpenQuestion holds

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Registered trials

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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.