Evidence map›Paper›PMID 34817389›Full record

Trial reportJournal of medical Internet research2021

Understanding the Relationship Between Official and Social Information About Infectious Disease: Experimental Analysis.

Elias Assaf, Robert M Bond, Skyler J Cranmer, Eloise E Kaizar, Lauren Ratliff Santoro, Susumu Shikano, David J Sivakoff

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2021. 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
0.4field-weighted citation impact, top 28% of its field
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, 3 citations in OpenAlex.

  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 at 3 institutions in 2 countries.

Elias AssafPharmacy Analytics and Consulting, Research Consulting, Humana, Louisville, KY, United States.ORCID 0000-0002-3143-1853
Robert M BondDepartment of Communication, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-5026-2186
Skyler J CranmerDepartment of Political Science, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-6213-6400
Eloise E KaizarThe Ohio State University, Columbus, OH, United States.ORCID 0000-0002-4397-5161
Lauren Ratliff SantoroThe University of Texas at Dallas, Richardson, TX, United States.ORCID 0000-0002-8914-4507
Susumu ShikanoUniversity of Konstanz, Konstanz, Germany.ORCID 0000-0003-4087-2789
David J SivakoffThe Ohio State University, Columbus, OH, United States.ORCID 0000-0001-6976-7814
The Ohio State University · USThe University of Texas at Dallas · USUniversity of Konstanz · DE

Funding

Development of a statistical framework for the analysis of social network predictors of therapeutic community outcomesR34DA043079 · NIDA · OHIO STATE UNIVERSITY · PI WARREN, KEITH LEVERETT · 2018 to 2020
$690k
NIDA NIH HHS R34 DA043079
6 · The paper itself

Abstract

backgroundCommunicating official public health information about infectious diseases is complicated by the fact that individuals receive much of their information from their social contacts, either via interpersonal interaction or social media, which can be prone to bias and misconception.

objectiveThis study aims to evaluate the effect of public health campaigns and the effect of socially communicated health information on learning about diseases simultaneously. Although extant literature addresses the effect of one source of information (official or social) or the other, it has not addressed the simultaneous interaction of official information (OI) and social information (SI) in an experimental setting.

methodsWe used a series of experiments that exposed participants to both OI and structured SI about the symptoms and spread of hepatitis C over a series of 10 rounds of computer-based interactions. Participants were randomly assigned to receive a high, low, or control intensity of OI and to receive accurate or inaccurate SI about the disease.

resultsA total of 195 participants consented to participate in the study. Of these respondents, 186 had complete responses across all ten experimental rounds, which corresponds to a 4.6% (9/195) nonresponse rate. The OI high intensity treatment increases learning over the control condition for all symptom and contagion questions when individuals have lower levels of baseline knowledge (all P values ≤.04). The accurate SI condition increased learning across experimental rounds over the inaccurate condition (all P values ≤.01). We find limited evidence of an interaction between official and SI about infectious diseases.

conclusionsThis project demonstrates that exposure to official public health information increases individuals' knowledge of the spread and symptoms of a disease. Socially shared information also facilitates the learning of accurate and inaccurate information, though to a lesser extent than exposure to OI. Although the effect of OI persists, preliminary results suggest that it can be degraded by persistent contradictory SI over time.

Indexed as

Communicable DiseasesSocial MediaHumansLearningPublic Healthdiseasenetwork experimentsofficial informationsocial information

Identifiers

PMID34817389
PMCPMC8663576
OpenAlexW3195501245

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.