Evidence map›Paper›PMID 36848256›Full record

ArticleJMIR human factors2023

Eliciting Opinions on Health Messaging During the COVID-19 Pandemic: Qualitative Survey Study.

Sienna Ruiz, Uzoma Charles Okere, Michelle Eggers, Catina O'Leary, Mary Politi, Fei Wan, Ashley J Housten

Abstract read
In one paragraph

Article in JMIR human factors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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.

Sienna RuizDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-5206-962X
Uzoma Charles OkereDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-7841-4903
Michelle EggersDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0009-0006-1194-0032
Catina O'LearyHealth Literacy Media, St. Louis, MO, United States.ORCID https://orcid.org/0009-0000-4779-1610
Mary PolitiDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0001-9103-6495
Fei WanDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-8311-5553
Ashley J HoustenDivision of Public Health Sciences, Department of Surgery, Washington University in St. Louis, St. Louis, MO, United States.ORCID https://orcid.org/0000-0002-7379-0678

Funding

WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
Mentored Education for Dissemination and Implementation Science (MEDIS) ProgramR25DK123008 · NIDDK · WASHINGTON UNIVERSITY · PI BROWNSON, ROSS C, HAIRE-JOSHU, DEBRA · 2020 to 2024
$1.1M
NCATS NIH HHS UL1 TR002345NIDDK NIH HHS R25 DK123008
6 · The paper itself

Abstract

backgroundEffective public health messaging has been necessary throughout the COVID-19 pandemic, but stakeholders have struggled to communicate critical information to the public, especially in different types of locations such as urban and rural areas.

objectiveThis study aimed to identify opportunities to improve COVID-19 messages for community distribution in rural and urban settings and to summarize the findings to inform future messaging.

methodsWe purposively sampled by region (urban or rural) and participant type (general public or health care professional) to survey participants about their opinions on 4 COVID-19 health messages. We designed open-ended survey questions and analyzed the data using pragmatic health equity implementation science approaches. Following the qualitative analysis of the survey responses, we designed refined COVID-19 messages incorporating participant feedback and redistributed them via a short survey.

resultsIn total, 67 participants consented and enrolled: 31 (46%) community participants from the rural Southeast Missouri Bootheel, 27 (40%) community participants from urban St Louis, and 9 (13%) health care professionals from St Louis. Overall, we found no qualitative differences between the responses of our urban and rural samples to the open-ended questions. Participants across groups wanted familiar COVID-19 protocols, personal choice in COVID-19 preventive behaviors, and clear source information. Health care professionals contextualized their suggestions within the specific needs of their patients. All groups suggested practices consistent with health-literate communications. We reached 83% (54/65) of the participants for message redistribution, and most had overwhelmingly positive responses to the refined messages.

conclusionsWe suggest convenient methods for community involvement in the creation of health messages by using a brief web-based survey. We identified areas of improvement for future health messaging, such as reaffirming the preventive practices advertised early in a crisis, framing messages such that they allow for personal choice of preventive behavior, highlighting well-known source information, using plain language, and crafting messages that are applicable to the readers' circumstances.

Indexed as

communicationCOVID-19disseminationhealth equityhealth informationhealth messagingimplementationmessagingpreventionrural populationsurban populations

Identifiers

PMID36848256
PMCPMC10176135

What OpenQuestion holds

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LicenceCC BY
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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.